The article's objective is to conduct theoretical research of modern digital solutions for risk management in business ecosystems and develop an intelligent digital tool for risk management under sustainable development conditions. A comprehensive analysis of modern technologies that include artificial intelligence, big data, and blockchain, is conducted, their role in improving risk management efficiency is determined. The research methodology combines both theoretical methods (systems analysis, comparative analysis, SWOT analysis) and empirical methods (statistical analysis, machine learning methods, and experimental research). The main categories of risks are systematized, and possibilities for their optimization through digital solutions are explored. The impact of digital technologies on achieving sustainable development goals is analyzed, particularly in aspects of efficient resource usage, social integration, and innovation development. Key challenges of digital transformation in risk management are identified, including cybersecurity issues and regulatory requirements compliance. The practical application of machine learning methods for predicting employee attrition is examined, demonstrating the potential of digital solutions in solving specific business challenges. A prediction system that uses various machine learning algorithms was developed and tested. A comparative analysis of the effectiveness of various machine learning algorithms for the prediction task was conducted. When selecting the optimal classifier, both standard quality metrics and probability distribution analysis for identifying risk groups were taken into account. A modular system structure is proposed, and practical recommendations for implementing digital solutions in business ecosystems are provided.

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