This study investigates the application of artificial intelligence (AI) methods, including neural networks (ANNs), long short-term memory (LSTM), convolutional neural networks (CNN), and hybrid CNN-LSTM models, to predict renewable energy production in Turkey. The primary objective is to enhance the accuracy and reliability of energy forecasts, which are crucial for optimizing power systems and integrating renewable energy sources into the grid. The research focuses on solar energy, a significant renewable resource in Turkey, and uses meteorological data to train and evaluate these models. The study compares the performance of various machine learning models, such as LightGBM, gradient boosting regressor (GBR), and random forest regressor (RF), alongside deep learning models. The results indicate that LightGBM outperforms other models in terms of accuracy, while the hybrid CNN-LSTM model has the highest rate of inaccuracy. The study also highlights the importance of accurate forecasting for managing the electricity market and ensuring fair pricing. The analysis covers the solar potential of Turkey, the photovoltaics applications, and the mathematical models used to predict solar energy production. The methodology involves data preprocessing, including handling missing values using the K-nearest neighbor (KNN) algorithm, and correlation analysis to understand the relationships between weather variables and solar energy output. The study develops and evaluates forecasting models using Python and the scikit-learn library, focusing on the root mean square error (RMSE) and mean absolute error (MAE) metrics. The findings provide valuable insights for researchers and practitioners in the field of renewable energy, particularly in developing countries like Turkey, where the transition to smart grids and renewable energy sources is ongoing. The study contributes to the literature by offering a comparative analysis of AI methods for renewable energy prediction and highlighting the practical implications for energy management and sustainability.This study investigates the application of artificial intelligence (AI) methods, including neural networks (ANNs), long short-term memory (LSTM), convolutional neural networks (CNN), and hybrid CNN-LSTM models, to predict renewable energy production in Turkey. The primary objective is to enhance the accuracy and reliability of energy forecasts, which are crucial for optimizing power systems and integrating renewable energy sources into the grid. The research focuses on solar energy, a significant renewable resource in Turkey, and uses meteorological data to train and evaluate these models. The study compares the performance of various machine learning models, such as LightGBM, gradient boosting regressor (GBR), and random forest regressor (RF), alongside deep learning models. The results indicate that LightGBM outperforms other models in terms of accuracy, while the hybrid CNN-LSTM model has the highest rate of inaccuracy. The study also highlights the importance of accurate forecasting for managing the electricity market and ensuring fair pricing. The analysis covers the solar potential of Turkey, the photovoltaics applications, and the mathematical models used to predict solar energy production. The methodology involves data preprocessing, including handling missing values using the K-nearest neighbor (KNN) algorithm, and correlation analysis to understand the relationships between weather variables and solar energy output. The study develops and evaluates forecasting models using Python and the scikit-learn library, focusing on the root mean square error (RMSE) and mean absolute error (MAE) metrics. The findings provide valuable insights for researchers and practitioners in the field of renewable energy, particularly in developing countries like Turkey, where the transition to smart grids and renewable energy sources is ongoing. The study contributes to the literature by offering a comparative analysis of AI methods for renewable energy prediction and highlighting the practical implications for energy management and sustainability.