<![CDATA[The growth in volume, velocity, and diversity of data has driven the need for analytical systems that are not only capable of handling big data, but also capable of generating intelligent predictions and insights through the integration of machine learning. This study aims to design and analyze a comprehensive framework that integrates machine learning algorithms into big data analytical systems. The research approach is carried out through literature studies and evaluations of various platforms and architectures such as Hadoop, Spark, and TensorFlow, which enable efficient large-scale data processing. The proposed framework includes the stages of ingestion, preprocessing, model training, evaluation, deployment, and feedback loops that support continuous learning. This integration not only improves the predictive capabilities of the system but also enables organizations to respond proactively to real-time data dynamics. The results of this study are expected to be a strategic reference in the development of modern data-driven analytical systems. ]]>
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