As solar power continues to be a crucial element of the global shift towards renewable energy, improving the performance of solar power plants becomes important. Various sensors installed on solar panels, inverters, and other equipment produce data, which is then integrated to create a virtual model, the digital twin, that reflects the solar power plant’s operational behavior. For precise representation and control, the system dynamically models the actions of the plant by integrating MATLAB-simulink simulations. Furthermore, fault detection is achieved using Neural Networks, Adaboost, Naïve Bayes, and Decision Trees. This unification of AI-based Digital Twins with energy generation prediction and forecasting, fault detection, and corrective maintenance technologies amplifies solar power plants' operational efficiency, reliability, and sustainability.