Indoor localization has attracted significant demand in diverse smart building applications like automated energy management, patient tracking in hospitals, industrial indoor navigation, etc. Most of the proposals use Wi-Fi access points to construct indoor localization systems and in such systems, the fundamental task is to deploy access points correctly. The existing literature has employed additional access points or related hardware to improve localization accuracy, which in turn results in expensive installation and maintenance costs. Our objective is to optimize deployment by modifying the positions of already existing access points without using any additional hardware. To achieve this, we propose a reverse multilateration based access point positioning framework that has three phases: the first phase uses multivariate regression to predict the coordinates of the target location based on received signal strength indicator values collected from multiple access points; the second phase identifies the misplaced access points using the cumulative error by distance ratio; and the third phase computes the new positions of access points through reverse multilateration. Experiments show that the proposal generates 888 correct predictions out of 960 data points, thereby improving the prediction accuracy by 4.79% when compared with existing methods.