Tunnel squeezing is characterized as a significant degree of distortion in the surrounding rock mass that is typically larger than the designed deformation. The squeezing potential of rocks around tunnels can result in support failures, floor heave, and even flood disasters. In this study, the squeezing potential of rocks around tunnels were estimated by employing a hybrid intelligent framework to improve the performance of a classification algorithm. A total of 139 adjacent rock-squeezing patterns were acquired from places such as China, Nepal, and India to form the empirical basis for this study. The data consists of five influential variables, i.e., strength factor, tunnel depth, rock mass quality index, tunnel equivalent diameter and support stiffness. The mechanism of prediction consisted of three steps. Firstly, factor analysis was utilized to reduce the number of influential variables. The resulting factors were then categorized using k-means clustering. Finally, a random forest algorithm was developed to predict various levels of surrounding rock squeezing potential of rocks around tunnels. The proposed hybrid intelligent framework achieved a strong predictive capability of 96%, contributing to safer and more sustainable tunneling practices by reducing operational risks and improving overall structural stability.