In recent years, researchers have shown significant interest in the vital role of wireless sensor networks. Sensor nodes have been used in a wide range of applications, including environmental monitoring, security applications, and target tracking. The latter involves the identification and surveillance of the target's motion. Research on the localization and tracking of multiple targets utilizing WSN has gained significant attention and posed as a complex and intriguing subject in recent years. While the literature has suggested several ways, further research is needed to develop more precise localization and tracking algorithms. Due to the abundance of customizable characteristics, it is challenging to discover a single approach that achieves optimum object recognition, localization, and tracking with a unique solution. Therefore, this study introduces the Radial Bias with Seeker Optimization (RBSO) technique, which uses the received signal strength index (RSSI) channel model to accurately locate and track targets in indoor environments. The investigation examines the outcome for the particle count in the RBSO algorithm, specifically for values of 5, 10, 15, 20, and 25. The simulation results demonstrate that the developed technique effectively decreases the particle count and enhances the speed of positioning and tracking, while maintaining the accuracy of target localization and tracking. It is seen that proposed RBSO achieves 13.2% of transmission error, 12.8% of ranging error, 83.5% of localization coverage, 93.2% of PDR, and 21.7% of energy consumption.