The rapid growth in global energy demand, coupled with increasing environmental concerns, has accelerated the transition toward renewable energy sources, with solar power emerging as one of the most promising and sustainable alternatives. Despite its advantages, the efficiency and performance of solar power plants are significantly influenced by dynamic environmental conditions such as solar irradiance, temperature variations, dust accumulation, cloud cover, and equipment degradation over time. Traditional monitoring and control mechanisms are often reactive, manual, and incapable of handling large-scale data, resulting in suboptimal performance and increased operational costs. In this context, Machine Learning (ML) has gained considerable attention as a powerful tool for enhancing the efficiency and reliability of solar energy systems This paper presents a comprehensive study on the application of Machine Learning techniques to improve the efficiency performance of solar power plants. The proposed approach utilizes data-driven models to analyze historical and real-time data collected from solar panels, sensors, and weather forecasting systems. Various supervised learning algorithms, including Linear Regression, Random Forest, and Support Vector Machines (SVM), are employed for accurate prediction of solar power generation and identification of performance patterns. Furthermore, advanced deep learning models such as Artificial Neural Networks (ANN) are implemented to handle complex nonlinear relationships between environmental variables and energy output. In addition to energy prediction, the system incorporates intelligent fault detection and predictive maintenance mechanisms. Machine Learning algorithms continuously monitor system parameters to detect anomalies such as panel degradation, inverter malfunctions, shading effects, and wiring faults. Early detection of such issues enables timely maintenance, reducing downtime and improving overall system reliability. The integration of predictive analytics also allows operators to optimize panel orientation, tilt angles, and tracking mechanisms, thereby maximizing energy capture throughout the day. The proposed ML-based framework is evaluated using a dataset comprising solar irradiance, temperature, humidity, and historical power output records. Experimental results demonstrate a significant improvement in prediction accuracy and operational efficiency compared to conventional methods. The system achieves up to 20–30% enhancement in energy output efficiency, along with a considerable reduction in maintenance costs and system failures. Additionally, real-time monitoring and automated decision- making contribute to improved scalability and adaptability of solar power plants.