Symbolic regression is a type of regression analysis that searches for mathematical expressions that best fit a given dataset, using symbolic expressions rather than predefined models. It is often implemented using genetic programming to explore a vast space of potential mathematical formulas, offering interpretable models that reveal underlying data patterns.