This study introduces a comparative evaluation of deep learning models for solar power prediction under diverse PV plant conditions, a topic not extensively covered in existing literature. Photovoltaic (PV) solar power plants are becoming a key renewable energy source, offering a sustainable solution for large-scale electrical power generation. These plants use solar radiation to produce electricity through PV panels that directly convert sunlight into electrical energy. Compared to concentrated solar power (CSP) systems, which are less efficient and more complex, PV technology provides a more practical approach for meeting increasing energy demands. This work applies to four machine learning models—LSTM, RNN, DNN, and CNN—to predict power generation in PV systems using datasets from normal and partially shaded conditions, along with two additional scenarios (LL1 and LL2). Each model’s performance was evaluated using RMSE and MAE metrics across three training sizes (80%, 60%, 40%). LSTM achieved the highest accuracy, particularly on simpler datasets (Normal PV and LL1), while RNN and CNN showed promise in more complex settings. DNN consistently underperformed in challenging environments. The findings support the integration of machine learning to enhance forecasting accuracy in PV systems and highlight LSTM’s robustness for real-world deployment.