<![CDATA[This study explores the integration of machine learning to optimize the discharge of Brushless DC (BLDC) motors in horizontal photovoltaic (PV) systems, designed to maximize solar radiation capture with the assistance of Maximum Power Point Tracking (MPPT) technology to enhance battery charging efficiency. Using the Random Forest algorithm, the research develops a predictive model to analyze the relationship between PV input power, battery status, and BLDC motor speed, achieving power classification accuracy of 74% and speed prediction with an R-squared value of 0.9124 and low error rates. System testing, which includes PV modules, batteries, BLDC motors, and MPPT, demonstrates successful integration under various operational conditions, while a PyQt5-based interface enhances user accessibility through interactive features. The findings make a significant contribution to renewable energy management, support electric vehicle efficiency, extend operational range, and reduce environmental impact.]]>
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