Maintaining the efficiency of solar photovoltaic (PV) systems is crucial for optimal energy production. Traditional invasive methods for diagnosing PV panel health are labor-intensive and time-consuming. This paper presents an advanced, non-invasive diagnostic approach that uses an enhanced ensemble classifier to identify faults, degradation, and performance issues in solar PV panels. By leveraging multiple machine learning models, the ensemble classifier accurately assesses the condition of panels based on non-invasive input parameters like voltage, current, and temperature. Simulation results validate the improved accuracy and robustness of the proposed method over individual classifiers.