Accurate State of Charge (SOC) estimation is crucial for the reliability, safety, and performance of lithium-ion (Li-ion) batteries, particularly in electric vehicles and energy storage systems. Conventional methods, such as Ampere-hour counting and Extended Kalman Filters (EKF), often struggle with the complex, non-linear, and dynamic behavior of Li-ion batteries. This paper introduces a novel hybrid approach that integrates Artificial Neural Networks (ANN) with Least Squares Support Vector Machines (LSSVM) to enhance SOC estimation. This fusion leverages the adaptability of ANN to various battery conditions and the robust generalization of LSSVM. The hybrid model is assessed using the Hybrid Pulse Power Characterization (HPPC) test, which mimics real-world driving conditions. The process—from data acquisition to model training and validation—is carried out in MATLAB/Simulink. Results demonstrate that combining ANN and LSSVM significantly improves SOC estimation accuracy and robustness compared to using either method independently. This integrated approach provides a more reliable and effective solution for advanced battery management systems, delivering superior SOC predictions across different operating conditions.