The accurate and cost-effective generation of TS geometries, at a significantly lower expense than current QM methods, presents a handsome reward -the key to unlocking crucial insights for understanding how chemical reactions occur. This review will focus on the current developments in the application of ML for predicting TSs. It will begin by outlining the methodologies through which ML can be used to tackle chemical problems.