This paper aims to optimize the installation of wind and solar energy infrastructure in Morocco through the strategic application of advanced Machine Learning (ML) techniques. The study critically evaluates state-of-the-art ML algorithms, including Support Vector Machines (SVM), tailored to the geographic characteristics of Morocco. The primary goal is to identify the most accurate ML model capable of determining optimal locations for wind and solar energy systems. This research significantly contributes to advancing forecasting methods in renewable energy applications, specifically tailored to the unique climatic and geographical characteristics of Morocco. The findings not only enhance our understanding of ML applications in renewable energy optimization but also offer practical insights for policymakers, energy planners, and investors seeking sustainable solutions for energy production in the region. In conclusion, the study underscores the effectiveness of ML in optimizing renewable energy deployment while emphasizing the importance of tailoring these approaches to the specific conditions of the target region. The findings provide a foundation for informed decision-making in the pursuit of sustainable and efficient renewable energy solutions in Morocco.