In Chap. 2  we introduced a hybrid volatility modeling framework based on the ARIMA–GARCH approach, which integrates traditional time-series and econometric techniques. While GARCH models have been the cornerstone of volatility modeling due to their ability to account for volatility clustering and persistence, they rely on parametric assumptions that might limit their flexibility. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI), particularly Artificial Neural Networks (ANNs), offer powerful, data-driven methods that can capture complex, nonlinear patterns and dependencies often present in financial time series but difficult to model with traditional methods. This chapter provides a brief introduction to ANNs and their applications, focusing on volatility modeling, to explore how these models can complement or enhance classical econometric frameworks like ARIMA–GARCH by potentially improving predictive accuracy and capturing nonlinearities without strict distributional assumptions.