Crypto-asset stability has become a central research topic as digital assets increasingly interact with global financial systems. Sharp volatility, sudden liquidity shocks, and the heterogeneous behavior of blockchain networks challenge traditional forecasting methods and highlight the need for machine-learning approaches capable of integrating diverse on chain, off chain, and behavioral signals. This article examines machine-learning frameworks for predicting crypto-asset stability and introduces an adaptive architecture developed by the author, described in an associated patent. The model integrates transaction graph signals, anomaly patterns, market microstructure indicators, regulatory lists, and sentiment data to generate real-time stability assessments. The study situates these developments within the evolving academic literature on volatility prediction, systemic risk, and anomaly detection, and proposes a formal methodology for combining heterogeneous features into stability scores. Empirical considerations highlight the importance of multi-modal data and dynamic model weighting. The article concludes with implications for risk management and regulatory oversight in digital-asset ecosystems.