<p>Software and frameworks for the development and application of machine learning, as well as the evolution of these tools over recent years. To conduct the review, the authors formulated three research questions and performed a comprehensive search covering the period from 2013 to 2025. The analysis identifies the most frequently implemented algorithms within Machine Learning (ML) frameworks and the software used to develop them, while also highlighting their principal advantages and disadvantages. Based on the findings of the literature review, the study proposes a generic algorithm for a framework capable of integrating multiple ML algorithms. The study concludes that developing a ML framework as a module for production-level software requires the convergence of two disciplines: ML and software engineering. When combined, these disciplines significantly enhance the performance of information systems.</p>