SymbolicRegression.jl performs distributed high-performance symbolic regression to discover mathematical equations from data. The library uses evolutionary algorithms and genetic programming to search for expressions that best fit a given objective. It integrates with the MLJ machine learning interface in Julia and provides a Python frontend via PySR. The tool is applicable to scientific machine learning (SciML), equation discovery, and interpretable modeling tasks.