The article presents a comprehensive study of modern artificial intelligence (AI) tools designed for intelligent data analysis (Data Mining), their computational capabilities, and areas of practical application. The key data analysis algorithms are examined, including machine learning methods, deep learning, clustering, classification, regression modeling, and neural networks. Special attention is given to tools such as Scikit-learn, TensorFlow, RapidMiner, Google AutoML, Power BI, and other platforms and frameworks that provide automation of data processing, analysis, and visualization of large-scale datasets. The study includes a comparative analysis of the advantages and limitations of the most widely used AI tools in terms of model accuracy, computational efficiency, ease of integration, scalability, AutoML support, and the ability to work with unstructured data. Examples of applying AI tools in industry, economics, medicine, the financial sector, and other fields of human activity are provided. The research considers current trends in the development of intelligent data analysis, taking into account the growing role of cloud platforms, automated model-building systems, multimo
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