Accurate prediction of moment capacity is critical for the safe and efficient design of reinforced concrete (RC) structures. Previous studies have investigated the flexural behaviour of RC beams reinforced with conventional steel and fiber-reinforced polymer (FRP) bars, showing that reinforcement ratio, material properties, beam dimensions, and concrete compressive strength significantly influence ultimate moment capacity. However, the nonlinear behaviour of concrete and the distinct characteristics of FRP continue to challenge precise prediction using conventional methods. This study evaluates the performance of artificial intelligence (AI) models developed in Python for predicting the moment capacity of simply supported RC beams reinforced with steel and FRP bars. The framework integrates data-driven modelling with systematic parametric analysis to assess the influence of reinforcement ratio, reinforcement type (Steel, CFRP, BFRP, AFRP, GFRP), beam cross-sectional dimensions, and concrete compressive strength (fc′). A control beam was used as a reference, and multiple design series examined sensitivity of moment capacity (Mu) to these parameters. The AI model was validated against previously published experimental studies to ensure reliability. Statistical indicators, including the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE), confirmed strong agreement between predicted and experimental values. Parametric analysis showed that increasing reinforcement ratio from 0.0019 to 0.0074 raised moment capacity from 68.00 kN·m to 259.89 kN·m. Beam dimensions and concrete strength also significantly affected performance, while differences among FRP types and steel highlighted material effects. The results demonstrate that Python-based AI modelling provides an accurate and efficient alternative to extensive experimental testing, offering a reliable data-driven tool for structural design and optimization of concrete beams.............. Keywords ..........:Reinforced Concrete (RC) Beams, Moment Capacity, FRP Reinforcement, Steel Reinforcement, Artificial Intelligence (AI), Python-Based Simulation.