This chapter introduces the Unscented Kalman Filter (UKF) as an advanced neural network training methodology that combines unscented transformation with Kalman filtering principles. Unlike Extended Kalman Filters that linearize nonlinear functions, UKF employs sigma points to accurately propagate statistical distributions through nonlinear systems. The chapter presents a comprehensive mathematical formulation of UKF algorithms, including state transition models, measurement functions, and covariance updates. A practical implementation demonstrates UKF’s application in target tracking scenarios with radar measurements in polar coordinates, showcasing its superiority in handling nonlinear observation models. The Python implementation using FilterPy library provides hands-on experience with UKF parameter tuning, sigma point generation, and real-time state estimation, establishing UKF as a robust alternative for neural network training in complex nonlinear environments.