Mineral prospectivity mapping is essential for identifying areas with significant potential for mineral deposits. Major challenges arise due to imbalanced data environments, which include sparse mineral occurrences and vast unexplored regions lacking confirmed "true negatives." This research presents a Bayesian Kernel Density Estimation (KDE) framework that incorporates spatial priors and geophysical feature similarity to tackle these challenges. The approach integrates: (1) spatial kernel density estimation (KDE) utilizing geographic proximity to established deposits, (2) feature-space KDE reflecting geophysical similarity, and (3) Bayesian fusion of these KDE models. The Bayesian KDE demonstrates a 72% reduction in false positives and a 240% enhancement in known-mineral detection relative to feature-only KDE. The evaluation of model performance was conducted using leave-one-out cross-validation (LOOCV) and intersection-over-union (IOU). LOOCV yielded an average percentile rank of 0.791, demonstrating robust predictive accuracy in the presence of data imbalance. The IOU analysis, performed at spatial resolutions ranging from 50 to 600 meters, demonstrated model stability and indicated strong concordance between full model predictions and individual leave-one-out cross-validation iterations. The approach necessitates prior spatial knowledge of mineral occurrences, which may introduce bias; however, extensive validation supports its practical applicability. The Bayesian KDE reduced potential zones to about 1% of the unexplored areas, thereby enhancing the efficiency of future exploration initiatives. The proposed method significantly surpassed standard KDE, enhancing the mean probability of known minerals by 240% and diminishing background noise, thereby offering an effective and validated strategy for mineral exploration in highly imbalanced datasets.