Bias in machine learning models is one of the biggest concerns in today’s AI-driven world. When models are trained on data that reflects real-world inequalities, they end up making unfair predictions that can harm people based on their gender, race, or age. This paper introduces FairScan, a two-stage framework designed to first detect and then actively reduce bias in classification models. The detection stage uses a new metric called the Statistical Parity Divergence Score (SPDS), which measures bias not just across individual groups but also at the intersections of multiple sensitive attributes. The mitigation stage applies a custom training strategy called Reweighted Fair Gradient Descent (RFGD), which adjusts how much the model learns from different groups during training to push it toward fairer outcomes. We tested our approach on the UCI Adult Income dataset and found that FairScan reduced the Demographic Parity Difference by up to 79.4% while maintaining a classification accuracy of 86.7%. Our results show that it is genuinely possible to build models that are both accurate and fair, which is a step forward for responsible AI development.