This paper reports a systematic study for evaluating the growth and enzyme kinetics of Candida antarctica MTCC 2706 for the first time. A hybrid machine learning (ML) aided experimental approach is proposed here to evaluate the microbial growth kinetics in which different ML models were built with a smaller set of experimental samples to predict the growth curve at different substrate concentrations and their performance was compared. Gradient boosting regression model was found to predict the growth curves effectively. Further, growth kinetics was evaluated and the data was found to be fitted well with Monod’s model. In addition, the activity of lipase produced in this study was evaluated, and the resulting enzyme kinetics were studied. Further, the statistical significance of models developed was quantified using average absolute deviation percentage (AAD %) and R2 parameters, and robustness of the model parameters is ensured through sensitivity analysis. The results of the proposed ML-based approach were found to be in good agreement with that of the conventional method while successfully reducing the experimental efforts, time and resources by nearly 50%. Also, the application of the proposed method can be extended to any other microbial process.