This paper explores advances in autonomous navigation, emphasizing its crucial role in self-driving vehicles, unmanned aerial vehicles, and spacecraft. The focus is on leveraging the Extended Kalman Filter (EKF) to enhance precision and reliability in non-linear dynamic systems. The EKF, an extension of the Kalman Filter, addresses real-world non-linearities by integrating linearization, enabling accurate state estimates. The study underscores the EKF's effectiveness through in-depth scenario analyses, showcasing its contribution to improved navigation accuracy. By applying the recursive EKF algorithm, which incorporates noisy sensor measurements and system dynamics, the research aims to elevate the reliability and precision of state estimation in dynamic systems. Notably, the study highlights the importance of linearizing equations for accurate state estimation in complex and dynamic environments