This article is devoted to the evaluation of artificial intelligence technologies for risk management in alternative investment firms. The relevance of the study is explained by the growing complexity of hedge funds, private equity, and private credit portfolios, where nonlinear risks and data scarcity challenge traditional models. The novelty of the research lies in a comparative analysis of supervised, unsupervised, natural language processing, and generative modeling approaches applied to financial risk forecasting and stress testing. The paper describes specific use cases of machine learning in value-at-risk estimation, default prediction, anomaly detection, and sentiment analysis. Particular emphasis is placed on the determining significance of explainable artificial intelligence in reinforcing transparency, regulatory adherence, and structured oversight, with the study aiming to illustrate how intelligent systems are capable of strengthening early detection mechanisms, minimizing anomaly incidence, and enhancing operational performance—while accounting for obstacles such as data integrity, model uncertainty, and integration barriers—by employing comparative and analytical frameworks applied to peer-reviewed academic literature and specialized industry documentation, and the concluding section identifies prerequisites for the responsible incorporation of AI solutions into alternative investment practices, offering practical value for scholars, supervisory bodies, market professionals, and policy developers seeking to align advanced technological instruments with reliable risk governance in the financial domain.