The prediction of mechanical properties in composite materials is crucial for optimizing their performance in various engineering applications. This study focuses on NaOH treated jute fiber reinforced glass epoxy composites, aiming to predict key mechanical characteristics like flexural strength, tensile strength, and the hardness. A comprehensive dataset of 974 samples was analyzed, encompassing a range of input parameters including fiber content, fiber orientation, matrix type, and processing conditions. Advanced statistical and machine learning techniques were employed to develop predictive models, with XGBoost showing itself to be the best model in terms of predicting each of the three mechanical properties. Feature importance analysis revealed that fiber content is the most significant factor influencing the predictions.