This review focussed on the application of ML for predicting TS geometries. It began by outlining the significance of the TS in computational chemistry, followed by an overview of available datasets for training ML models in TS geometry prediction. The review then highlighted the role of ML in predicting crucial reaction properties, such as activation energies, and emphasised the importance of accurate molecular geometries for enhancing model performance. Various ML approaches for predicting TS geometries were presented, including double-ended methods that utilise 3D reactant and product geometries, 2D graph-based techniques, RL strategies, and an MLP specifically trained on TS data.