Data-driven modeling is a methodological paradigm focused on constructing models primarily by extracting patterns, relationships, and structures directly from empirical data. This approach leverages computational and statistical techniques to understand, predict, and simulate complex phenomena, offering insights derived from observed system behavior rather than solely from first principles. Its significance lies in enabling robust analysis and prediction in domains where comprehensive theoretical frameworks are challenging to establish or insufficient for capturing intricate system dynamics.