Drug discovery and development have significantly benefited from conventional methods for optimising chemical processes, which are essential for synthesising and improving prospective pharmacological compounds ( ). These methods rely on well-founded ideas of organic chemistry, which include empirical rules and refined reaction conditions throughout decades of laboratory work. The optimised reaction conditions maximise desired product reaction yields, purity, and selectivity through exact control of variables, including temperature, pressure, and catalysts. Although the optimisation of chemical reactions that are expensive and ineffective is still an essential feature of pharmaceutical synthesis, the conventional approach has generated a significant number of possible medications ( ). Luckily, ML applications in chemical reaction optimisation can efficiently solve the restrictions of conventional approaches. Therefore, machine learning applications overcome the limitations of conventional approaches, such as LabMate.ML ( ) is shown in Figure . LabMate.ML simplifies the process of optimising reaction conditions using a repetitive computational method. At first, the user inputs reactants and reaction parameters into LabMate.ML, which is shown as the left pathway in the picture. The software begins with an initial exploration phase (iterations 1-10), during which it selects starting experiments based on predictions that have the most considerable variability in output without considering the target value (measured AUC in LC-MS traces). The exploratory phase enables LabMate.ML will collect various data points and enhance its predictive model. During iterations 11-20, LabMate.ML adopts an exploitative (greedy) strategy, prioritising selecting conditions that maximise the target value of the reaction output. The selection approach entails selecting reactions with the most minor variability among the top five anticipated high-yielding reactions, given that the highest target value from the exploratory phase is four times higher than that of randomly chosen starting trials. Alternatively, if the goal value fails to exceed this threshold, LabMate.ML chooses conditions with the most significant variability among the top ten predictions that generate high results. During each iteration, LabMate.ML consistently enhances its predictive model by retraining it with fresh data points, optimising hyperparameters (such as the number of trees, tree depth, and amount of features in RF regressors), and improving forecasts for untested circumstances that still need to be evaluated-the optimised synthesis protocol derived by LabMate.ML following repeated refinement is represented by the right pathway in the picture. This protocol enables efficient and informed experiment selection without requiring specialised gear. This method democratises the field of synthetic chemistry by combining chemical intuition with computational automation, resulting in improved efficiency and reduced resource usage ( ).