An investigation into the prediction of thermal runaway in lithium-ion batteries subjected to abusive mechanical loading is comparable to a crash or impact test and a crucial safety measure that can prevent catastrophic events. Using an explicit crash simulation method, it is possible to simulate the indentation test model and predict the thermal runaway. However, it is important to note that this approach is associated with a significant time investment. State-of-the-art technologies that involve machine learning for prediction of thermal runaway can be utilised for faster predictions. The research conducted in this thesis focuses on a cell model that simulates an indentation test and a machine learning model that predicts the thermal runaway. This model successfully captures the behavior of an internal short circuit and is verified using experimental data from existing literature. Additionally, a workflow is generated in Altair Hyperstudy to produce data essential for training the machine learning model using an automated process. This data created is based on the design variables of the indenter (impacting body). This facilitates comprehension of potential deformation, damage and related patterns. The machine learning model is created using the Altair Physics AI tool and subsequently trained by the provided dataset. Data, as foundational resource is used to train the machine learning model. These datasets must be available in adequate quantities and of high quality for neural network training to discover the relationship between input and output. The outcomes of this Machine learning model yield an adequate degree of accuracy. The accuracy of these results is highly dependent on the model, quality of data derived from this dataset and the Hyperparameters used for the model training. Insights of the predicted results by ML model are of great use for Design consideration and validation of lithium-ion batteries. The outcomes of conducting the indentation test simulation on the cell jellyroll model provides insights on the potential time in which a thermal runaway will occur, which could potentially lead to the occurrence of a fire or explosion. Moreover, the development of a comprehensive scaled model incorporating relevant data holds significant implications for future battery regulations.