This research aimed to develop a comprehensive, data-driven machine learning (ML) models for predicting overall gas holdup in slurry bubble column reactors (SBCRs), addressing a critical challenge. A robust dataset comprising 1938 experimental instances was systematically compiled from 20 peer-reviewed publications (1980-2022) under diverse conditions (d_p,ρ_p,〖C_v,M_A,ρ_g,σ_l,μ_l,ρ〗_l,T,P,u_g,AR,D_c,S_T,ζ). Seven ML models (Extra Trees (EXT), Random Forest Tree (RFT), Adaboost, Gradient Boosting, Extreme Gradient Boosting (XGBoost), SVR, and DNN) were rigorously trained and evaluated for gas holdup prediction. The ML models developed in this work underwent rigorous evaluation, including comparative analysis with existing literature correlations, experimental validation, and sensitivity assessment. The predictability and generalization capabilities of the ML models were found to be excellent compared to the existing literature correlations. Furthermore, a Graphical User Interface (GUI) was developed to rapidly predict overall gas holdup for industrial applications, providing engineers with an advanced predictive tool for SBCR modeling, design, and optimization.