This study pioneers the application of machine learning (ML) for predicting the bearing strength of double shear bolted connections in structural steel. For the first time, a comprehensive database comprising 443 experimental datasets was compiled, with input features including the normalized end distance, edge distance, bolt pitch along and transverse to the loading directions of the connection, ultimate-to-yield strength ratio of the steel plate, number of bolt rows, connection configuration and normalized bearing capacity. Eleven ML techniques were explored for this application. Feature importance analysis identified the normalized end and edge distances as the most influential parameters on the ultimate bearing capacity. The performance of the models was evaluated using various statistical metrics and compared with existing formulations and design code provisions. Among all ML models, Random Forest was the best performing model, attaining the highest coefficient of determination (0.88), lowest mean absolute error (0.14), and lowest mean square error (0.26). Unlike existing models that are specific to certain steel grades and provide different equations for different failure modes, ML models accomplished an integrated and generalized predictive approach with an acceptable level of accuracy. Interestingly, ML models revealed that the ultimate-to-yield strength ratio of steel and the numbers of bolt rows, which are currently ignored by design guidelines, do influence the bearing strength significantly (nearly 10% each). A user-friendly interface comprising all proposed ML algorithms was developed to ease the design process of double shear bolted connections and serve as an educational and research tool for applying ML techniques to predicting the bearing strength of double shear bolted connections.