This thesis investigates neural-based learning methods for vehicle dynamics estimation and control, with emphasis on side slip angle estimation and traction control in high-performance vehicles. Traditional control strategies, based on analytical models and offline calibration, are often limited by uncertainties, sensor noise, and rapidly changing conditions. Data-driven approaches using Deep Learning (DL), Reinforcement Learning (RL), and Neural Model Predictive Control (NMPC) offer greater adaptability, robustness, and accuracy in these scenarios. The first part focuses on estimating the lateral dynamics of a racing vehicle using neural architectures such as Feedforward, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) networks. These models were trained and validated through simulations using telemetry data from Podium Advanced Technologies, demonstrating their capability to function as reliable virtual sensors for state estimation.