The BIPC framework reduces solution time for large-scale mixed-integer programs by identifying a backdoor subset of variables that drive computational complexity. Using supervised learning, it predicts backdoor variable values and intervals, then solves a reduced problem with these predictions, achieving significant speedups with minimal quality loss. This enables rapid, high-quality solutions under parameter perturbations in real-world systems like power and supply chains.