In this investigation GA (Genetic Algorithms) and ANN (Artificial Neural Networks) was used for predicting surface roughness in CNC-machined aluminium (Al356) components based on machining parameters viz. feed rate (FR), depth of cut (DOC), and cutting velocity (CV) which shows the effectiveness of hybridizing these two computational intelligence techniques. Thus ANN's universal function approximation capability to capture complex relationships between input parameters and surface roughness has been used, while GA was used to optimizes the initial weights and biases of the ANN to prevent convergence to the local minima and enhance global optimization. Hybrid ANN-GA model shows the better performance in comparison with conventional ANN and Nonlinear Regression (NLR) using multiple statistical metrics. The results establish that integrating GA with ANN develops convergence speed and prediction accuracy. The trained hybrid ANN-GA model can efficiently estimate surface roughness, enabling operators to optimize machining parameters for improved efficiency and product quality without extensive experimental runs. For lowest RMSE and MAPE values GA-ANN hybrid model was used. The hybrid ANN-GA model outperformed all approaches, yielding the best prediction accuracy with R² = 0.95, RMSE = 0.0059 µm, and MAPE 1.2%. Furthermore, the hybrid model identified optimized machining parameters—Feed = 0.108 mm/rev, Depth of Cut = 0.266 mm, and Cutting Velocity = 1860.6 m/min—resulting in the lowest predicted surface roughness of 0.0168 µm. These findings highlight the superiority of the ANN-GA hybrid framework for precision machining optimization, providing both predictive accuracy and practical process guidelines. Results from this investigation can aid in enhancing manufacturing efficiency and product-quality in correctness machining of aluminium components.