ML emerged as a crucial tool in the development of reactive models in computational chemistry. Traditional computational chemistry methods often rely on approximate QM calculations or FF to simulate reactions, which can be computationally expensive or limited in their accuracy. ML techniques offer an alternative approach by leveraging large datasets of QM calculations or experimental reaction data to learn the underlying relationships between molecular structures, reaction conditions, and reaction outcomes.