The principal objective in the treatment of e-waste is to capture the bromine released from the brominated flame retardants (BFRs) added to the polymeric constituents of printed circuits boards (PCBs) and to produce pure bromine-free hydrocarbons. Metal oxides such as calcium hydroxide (Ca(OH)2) have been shown to exhibit high debromination capacity when added to BFRs in e-waste and capturing the released HBr. Tetrabromobisphenol A (TBBA) is the most commonly utilized model compound as a representative for BFRs. Our coauthors had previously studied the pyrolytic and oxidative decomposition of the TBBA:Ca(OH)2 mixture at four different heating rates, 5, 10, 15, and 20 °C/min, using a thermogravimetric (TGA) analyzer and reported the mass loss data between room temperature and 800 °C. However, in the current work, we applied different machine learning (ML) and chemometric techniques involving regression models to predict the TGA data at different heating rates. The motivation of this work was to reproduce the TGA data with high accuracy in order to eliminate the physical need of the instrument itself, so that this could save significant experimental time involving sample preparation and subsequently minimizing human errors. The novelty of our work lies in the application of ML techniques to predict the TGA data from e-waste pyrolysis since this has not been conducted previously. The significance of our work lies in the fact that e-waste is ever increasing, and predicting the mass loss curves faster will enable better compositional analysis of the e-waste samples in the industry. Three ML models were employed in our work, namely Linear, random forest (RF), and support vector regression (SVR), out of which the RF method exhibited the highest coefficient of determination (R2) of 0.999 and least error of prediction as estimated by the root mean squared error (RMSEP) at all 4 heating rates for both pyrolysis and oxidation conditions. An 80:20 split was used for calibration and validation data sets. Furthermore, for showing versatility and robustness of the best-predicting RF model, it was also trained using all the data points in the lower heating rates of 5 and 10 °C/min and predicted on all the data points for the higher heating rates of 15 and 20 °C/min to again obtain a high R2 of 0.999. The excellent performance of the RF model showed that ML techniques can be used to eliminate the physical use of TGA equipment, thus saving experimental time and potential human errors, and can further be applied in other real-time e-waste recycling scenarios.