This study presents an algorithm for mobile node localization in wireless sensor networks, leveraging the Extended Kalman Filter (EKF). The algorithm demonstrates robustness in handling non-linear dynamics and adaptability to varying environmental conditions. While initial conditions and Gaussian noise assumptions pose challenges, ongoing efforts aim to address these limitations. Future directions involve the refinement of sensor models, exploration of multi-sensor fusion, integration of machine learning techniques, and rigorous real-world testing. The algorithm's potential for three-dimensional localization and energy-efficient strategies positions it as a promising solution for dynamic scenarios. This research contributes to the advancement of mobile node localization methodologies, providing insights into its strengths, limitations, and avenues for future improvement.