PythiaCHEM is a modular toolkit implemented in Python v.3.7 and organized within Jupyter Notebooks. PythiaCHEM offers a range of ML algorithms through scikit-learn, including LASSO with cross-validation (LASSOCV), Logistic regression (LR), Support Vector Machines(SVM), k-Nearest Neighbors (kNN), Gaussian Process (GP), Bayesian Regression, Decision Trees (DT), random forest (RF), Extra Trees (ET), and AdaBoost. These algorithms have been selected for their ability to handle high-dimensional data, robustness against noise and outliers, and computational efficiency. While XGBoost was initially included, it was later excluded from the default notebooks due to its higher computational cost. However, users have the flexibility to incorporate additional ML algorithms based on their needs.