In this study, we implemented a parking occupancy/vacancy detection system (POVD) in a scaled-down model of a parking system for commercial centers by employing multiple WiFi access points. By exploiting the presence of WiFi routers installed in a commercial establishment, the WiFi’s received signal strength indicator (RSSI) signals were collected to establish the parking fingerprints and then later used to predict the number of occupied/vacant slots. Our extensive experiments were divided into two phases, namely: offline training and online matching phases. During the offline stage, the POVD collects available WiFi RSSI readings to determine the parking lot’s fingerprint based on a given scenario and stores them in a fingerprint database that can be updated periodically. On the other hand, the online stage predicts the number of available parking slots based on the actual scenario compared to the stored database. We utilized multiple router setups in generating WiFi signals and exhaustively considered all possible parking scenarios given the combination of 10 maximum access points and 10 cars. From two testing locations, our results showed that, given a parking area dimension of 13.40 m2 and 6.30 m2 and with the deployment of 4 and 10 routers, our system acquired the best accuracy of 88.18% and 100%, respectively. Moreover, the developed system serves as experiential evidence on how to exploit the available WiFi RSSI readings towards the realization of a smart parking system.