Wi-Fi fingerprinting indoor positioning systems (FP-IPSs) using RSSI are essential for indoor location-based services where GPS fails. This study evaluates four KNN algorithms (Traditional, Regions-based, Weighted Average, and Median Filtering) for RSSI-based Wi-Fi FP-IPS at University West’s campus. Metrics assessed include accuracy, precision, and computational cost. The Regions-based algorithm excelled with an average error of 5.2 meters and a prediction time of 0.01 seconds. In contrast, the Traditional algorithm had higher errors (18.4 meters) but similar efficiency (0.01 seconds). Weighted Average and Median Filtering algorithms offered a balance between accuracy and cost. These findings highlight the regions-based algorithm’s efficiency and accuracy for real-world applications.