This study pioneers a novel Python-based computational framework to optimize the sustainable production of 1,3-butadiene (BD) from bioethanol. The core innovation lies in the synergistic integration of kinetic modeling, thermodynamics, and machine learning (ML) with an optimized K₂O:ZrO₂:ZnO/MgO-SiO₂ catalyst. This catalyst was selected for delivering the highest combined BD and acetaldehyde selectivity (72 mol%) while maintaining reasonable BD yield and productivity (0.12 g_BD·g_cat^-1·h^-1), outperforming Na and Li analogues primarily due to better surface area retention, thereby enhancing BD selectivity and minimizing byproducts. This holistic approach elucidates how temperature (300-400 °C), weight hourly space velocity (WHSV: 0.3-2.5 h^-1), and ethanol feed fraction (0.41-0.85) govern process efficiency. Key findings confirm the reaction's endothermic nature and a strong correlation between thermodynamic driving forces (Gibbs free energy ΔG = -25.3 to -10.5 kJ·mol^-1) and productivity. Optimal conditions (350-375 °C, WHSV 0.93-1.24 h^-1) maximized BD yield at 25.3%, significantly reducing byproducts compared to non-optimal settings where acetaldehyde selectivity reached 57.3%. Among ML models, Random Forest excelled (R² = 0.91 for ethanol conversion prediction), attributed to its superior handling of complex, nonlinear variable interactions, with temperature and feedstock composition identified as dominant factors. The methodology provides a practical computational toolkit for catalyst and reactor design, explicitly addressing the critical trade-off between productivity (reaching 0.49 g_BD·g_cat^-1·h^-1 at high WHSV) and yield. By enabling data-driven optimization of feed control and catalyst efficiency, this work offers a powerful strategy for advancing renewable chemical manufacturing and decarbonizing the production of critical precursors such as BD for synthetic rubber and plastics.