10 Machine Learning Tools That Decode On-Chain Data Like A Pro In 2025
In Brief
Machine learning is transforming on-chain analysis by helping advanced users decode complex blockchain activity, uncover hidden patterns, and gain actionable insights.
On-chain analysis gets harder every year: more chains, more transactions, more complex behaviors, and far more noise than any human can manually decode. But modern machine learning tools are changing that. They sift through massive blockchain datasets, spot hidden patterns, map entities, and surface insights that traditional heuristics simply miss.
Below are ten of the most impactful ML-powered tools that help advanced users decode on-chain data with clarity, precision, and depth.
Nansen
Alt cap: Nansen logo showing a simple, abstract teal shape with four rounded, intersecting loops forming a symmetrical design on a white background.
Nansen is one of the earliest and most influential platforms to bring machine learning into mainstream on-chain analytics. At its core, Nansen uses ML-driven wallet clustering that groups blockchain addresses into identifiable entities and behavioral categories.
Such models handle enormous transaction graphs, identifying similarities among a vast number of interactions in order to draw conclusions about the ownership of wallets—whether they are the exchanges, market makers, DAO treasuries, smart money traders, or NFT communities.
What makes Nansen unique is the scale and quality of its labeled datasets. Its entity tagging is built using probabilistic models trained on years of historical activity, refined through both supervised and unsupervised ML.
The outcome is a degree of clarity regarding wallet identities that only a handful of platforms can compete with. Nansen offers advanced users like fund managers, analysts and quant traders the tools such as behavioral dashboards, cohort analysis and real-time alerts that inform how the big players are shifting their assets across chains.
Arkham Intelligence
Alt cap: White geometric logo resembling an abstract A next to the word ARKHAM in bold, white capital letters on a black background.
Arkham Intelligence brings an intelligence-agency mindset to blockchain data, leaning heavily on machine learning to deanonymize and map transactions at unprecedented depth. The platform uses graph neural networks and custom ML models to cluster addresses, discover links between wallets, and uncover the entities behind major flows.
Arkham’s interface resembles investigative software, surfacing network graphs that show how capital moves between trading firms, OTC desks, exchanges, and private wallets.
Its ML systems excel at identifying subtle relationships—multi-hop pathways, dormant wallet reactivations, or coordinated movement patterns that would be nearly impossible for analysts to track manually.
Arkham focuses intensely on identity resolution, giving advanced users a fine-grained view of who is actually active on the chain rather than just what is happening.
Chainalysis Reactor
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Chainalysis Reactor is one of the most widely used tools in the world for tracking illicit activity, regulatory compliance, and high-risk capital flows. While it’s best known for law enforcement use, the underlying machine learning framework is powerful and relevant for advanced on-chain researchers as well.
Reactor uses ML to classify risk levels, score transactions, and detect suspicious patterns across historical and real-time blockchain activity. Supervised learning models are trained on datasets featuring known fraud typologies, AML patterns, darknet market transactions, sanctions-related addresses, and money-laundering strategies.
Because Reactor’s ML models must meet regulatory standards, its clustering and anomaly detection tend to be extremely robust. For analysts who need high-confidence entity mapping—especially in DeFi exploit investigations or tracking complex fund flows—Chainalysis remains a top-tier tool.
Glassnode
Alt cap: Glassnode logo showing a lowercase white letter g centered on a solid black background.
Glassnode has become a cornerstone for macro-level on-chain analytics, and much of its most sophisticated data relies on machine learning. ML is embedded in features such as entity-adjusted supply, wallet segmentation, long-term holder analysis, cohort behavior metrics, and liquidity structure modeling.
Glassnode’s ML models use probabilistic heuristics to determine which addresses belong to the same entity and how wallet groups behave across market cycles. This enables the platform to generate advanced indicators, such as supply concentration among long-term holders, liquidity migration between cohorts, or reactions to macro events.
Glassnode focuses on long-horizon behavioral patterns. ML is used less for real-time alerts and more for structural insight—perfect for analysts looking to understand market phases rather than day-to-day noise.
Sentora
Alt cap: Sentora logo showing a stylized white outline of a centaur drawing a bow, set against a solid blue background, with a registered trademark symbol near the hind legs.
Sentora blends on-chain, off-chain, and market data through a wide array of ML-powered indicators. The platform runs ML classification models, sentiment analysis engines, clustering algorithms, and predictive systems to generate insights that go beyond raw blockchain metrics.
Its tools cover everything from whale accumulation to directional price signals, liquidity behavior, social sentiment, order book flows, and capital rotation indicators. Sentora’s ML models work across multiple data domains, making it one of the few platforms where analysts can simultaneously evaluate blockchain activity, exchange depth, and market psychology.
With Sentora’s holistic approach, ML signals are not siloed—they are stitched together to present a multi-dimensional view of the market, giving advanced users a richer context for decision-making.
Elliptic Lens
Alt cap: The word ELLIPTIC is written in bold, uppercase letters with a blocky, geometric font. The letters have a white fill with black outlines, giving a three-dimensional effect.
Elliptic is heavily focused on risk scoring and compliance, and its machine learning infrastructure reflects that mission. Elliptic Lens uses ML-based anomaly detection and supervised classification systems trained on proprietary datasets involving illicit finance patterns.
Its models identify high-risk wallets, classify transaction clusters, and flag unusual flows that could indicate fraud, scams, or laundering activity. Because Elliptic works directly with financial institutions and regulatory bodies, its ML systems are tuned for high precision and interpretability.
The main factor is the breadth of its proprietary data, which the ML models use as training material. For analysts investigating hacks, fraud, or suspicious activity across chains, Elliptic provides clean, reliable, regulator-grade intelligence.
TRM Labs
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TRM Labs specializes in cross-chain intelligence and uses ML models to detect laundering typologies, reconstruct multichain transaction paths, and identify coordinated activity across ecosystems.
Its ML systems excel in linking wallets across multiple networks—a necessity as funds increasingly move through bridges, Layer-2 rollups, and privacy-enhancing tools. TRM’s clustering models also identify unusual fund flow structures and multi-hop routing often used to obscure asset origins.
While many platforms excel on a single chain, TRM is one of the strongest tools for analyzing capital that moves fluidly across several networks.
Footprint Analytics
Alt cap: Logo for Footprint Analytics, featuring colorful overlapping foot shapes forming a circular pattern on the left, with the text Footprint Analytics in bold purple letters on the right.
Footprint Analytics uses machine learning primarily to solve one of the hardest problems in crypto: data cleanliness. On-chain data is notoriously messy—addresses are duplicated, contract interactions are ambiguous, and different blockchains structure data differently.
Footprint’s ML models automatically clean, normalize, and standardize raw blockchain data across many ecosystems. They resolve entity relationships, deduplicate wallets, classify contract activity, and structure data into dashboards that users can query without worrying about inaccuracies.
For advanced analysts building complex dashboards or comparing ecosystems, Footprint’s ML-driven normalization ensures that the underlying data is trustworthy—a critical requirement for high-level research.
Moralis ML Insights / ML-Enhanced Data Streams
Alt cap: Moralis logo showing a stylized heart shape in a gradient of blue, purple, and pink on a white background, with smooth curves and a modern, minimalistic design.
Moralis focuses on delivering ML intelligence directly to developers, making it possible to integrate on-chain ML insights into apps, bots, dashboards, or automated systems.
Its ML models classify wallet behavior in real time, tag contract events, and enhance streaming blockchain data with behavioral signals. This gives builders powerful ways to create trading bots, analytics dashboards, notification systems, and automated workflows that rely on real-time ML interpretation.
Moralis stands out because it bridges ML analytics with developer pragmatism. Rather than presenting dashboards, it offers ML-enhanced data streams that can be integrated directly into products.
Dune + Community ML Pipelines
Alt cap: Dune logo featuring a circle split diagonally into orange (top left) and dark blue (bottom right) next to the word “Dune” in bold black text on a light background.
While Dune is not inherently a machine learning platform, its flexible data environment has made it a favorite for analysts who build their own ML pipelines. Advanced users often export Dune query results to Python or ML environments, run clustering or predictive models, and then feed the results back into Dune dashboards.
Community-driven ML extensions—scripts, models, and notebooks—now classify contract interactions, tag wallet behaviors, and even forecast activity trends. This DIY-ML workflow makes Dune uniquely adaptable: users can create extremely specialized machine learning analytics for niche ecosystems, emerging tokens, or experiment-heavy DeFi protocols.
For power users, Dune offers the richest sandbox for custom ML on-chain analysis.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
