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With the broad establishment of WiFi passageways, the WiFi-based indoor localization approach has become one of the most generally utilized area advances. The existing method for WiFi-based indoor localization mostly adopts the classic fingerprinting approach with Received Signal Strength (RSS) and Time of Arrival (ToA). The proposed work compares three different localization algorithms such as the Multilateration method, K-Nearest neighbour (KNN) and Minimum Mean Square Error (MMSE) approach with RSS for indoor WiFi. Thus, simulating the various localization algorithms in order to infer a degree of correlation between actual data and estimated locations and hence their error or improved accuracy. The simulated result showcases the performance and bottlenecks of each and every algorithm with visual evidence by using Network Simulator (NS-2).