For efficient grid operation and energy management, accurate forecasting of solar radiation is essential. The unpredictable nature of weather makes this task challenging to accomplish. Existing forecasting models fail to deliver accurate results under these conditions, which results in decreased operational efficiency for renewable energy systems. We are proposing a novel methodology that combines feature engineering, machine learning, and Bayesian Optimization (BO) to obtain optimal performance. First, time frequency characteristics are extracted using a Fast Fourier Transform (FFT)-based feature engineering approach to capture dominant patterns from meteorological data. The FFT features reveal essential periodic patterns, which describe solar irradiance and its associated variables, enabling models to perform better over different time periods. The model hyperparameter tuning process, which uses Bayesian Optimization, improves prediction results. Model performance is evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². The results show clear improvements across Random Forest (RF), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) models, with the MLP model achieving the strongest overall performance. Specifically, the MLP achieved an R² value of 0.92, with MAE and RMSE values of 1.78 and 2.75, respectively. The proposed method also demonstrates robustness under varying weather conditions and time-series cross-validation (TSCV). Overall, the combined effects of frequency-domain feature engineering and Bayesian Optimization enable robust and adaptive forecasting of solar radiation resources.