Battery State-of-Health (SoH) estimation is essential for ensuring the reliability, safety, and cost-effectiveness of lithium-ion batteries in electric vehicles. Traditional approaches often require detailed knowledge of battery degradation mechanisms, making them computationally expensive and difficult to generalize. This paper introduces a data-driven methodology focused on lightweight features extracted exclusively from the final portion of the constant current (CC) charging phase, commonly observed in real-world scenarios. Statistical and shape-based features, such as mean, standard deviation, skewness, kurtosis, charging duration, accumulated charge, slope, and entropy, were computed from voltage and current signals. Several machine learning models, including Support Vector Regressors, Random Forest, Linear Regression, K-Nearest Neighbors, and Multi-Layer Perceptrons, were evaluated for accuracy, computational complexity, and suitability for embedded deployment. Extensive experimentation on a dataset comprising 92 lithium-ion cells demonstrates that the proposed approach achieves robust predictive performance, with mean absolute percentage errors around 1%. The Multi-Layer Perceptron emerged as a balanced solution, offering a favorable trade-off between accuracy and computational efficiency, making it highly suitable for real-time, embedded battery management systems.