Accurate parameter estimation of lithium-ion battery Equivalent Circuit Models (ECMs) is essential for precise State-of-Charge (SOC) estimation and optimized control in Battery Management Systems (BMS). Conventional estimation methods face limitations when modeling nonlinear and time-varying battery characteristics, and they require extensive experimental data. To address these challenges, this paper presents a novel hybrid deep-learning framework that integrates a Long Short-Term Memory-Recurrent Neural Network (Hybrid LSTM-RNN) with Transfer Learning (TL) for enhanced parameter estimation of the Second-Order (2-RC) ECM. The proposed architecture leverages LSTM capabilities to learn long-term temporal dependencies in current-voltage sequences, while TL improves generalization across different battery domains with minimal target data. Experimental validation using Hybrid Pulse Power Characterization (HPPC) data demonstrates significantly improved estimation performance across multiple C-rates, achieving MAE – 0.04315 and RMSE – 0.06633. Results confirm the robustness, high accuracy, and rapid convergence capability of the proposed approach in extracting dynamic ECM parameters. The framework provides a practical, scalable, and data-efficient solution for advanced lithium-ion battery modeling in electric-vehicle applications.