The k-Nearest Neighbors algorithm, a fundamental machine learning technique, typically employs the Euclidean distance metric for proximity-based data classification. This research focuses on the Feature Importance Infused k-Nearest Neighbors model, an advanced form of k-Nearest Neighbors. Diverging from traditional algorithm uniform weighted Euclidean distance, Feature Importance Infused k-Nearest Neighbors introduces a specialized distance weighting system. This system emphasizes critical features while reducing the impact of lesser ones, thereby enhancing classification accuracy. Empirical studies indicate a 1,7% average accuracy improvement with proposed model over conventional model, attributed to its effective handling of feature importance in distance calculations. Notably, a significant positive correlation was observed between the disparity in feature importance levels and the model's accuracy, highlighting proposed model's proficiency in handling variables with limited explanatory power. These findings suggest proposed model's potential and open avenues for future research, particularly in refining its feature importance weighting mechanism, broadening dataset applicability, and examining its compatibility with different distance metrics.