This study focused on the predictive models incorporating machine learning techniques that induce new dynamics for forecasting energy generation, enabling effective planning, financing, and system monitoring. The research developed a machine learning-based power generation prediction model tailored explicitly for Kenya's Garissa solar power plant. The selected model demonstrated a root mean squared error of 5.23 during evaluation, resulting in a prediction accuracy of 90.42%. This high accuracy indicates that the model can be relied upon for precise generation prediction, facilitating effective planning, and system performance monitoring