The layout of underground engineering objects significantly influences the stability of the surrounding rock mass and construction safety. Despite advancements toward intellectualization and informatization in design optimization and safety assessments, mechanical analysis-based engineering computations still face certain impediments. Consequently, this paper proposes a comprehensive framework integrating tunnel information modelling (TIM), finite element method (FEM) and machine learning (ML) technology to optimize the tunnel longitudinal orientation. It also delves into the specifics of addressing the challenges associated with each technology. The framework encompasses three phases: parametric modelling based on TIM, automatic numerical simulation based on FEM, and intelligent optimization leveraging ML. Initially, geometric models of the geological formations and engineering structures are constructed on the TIM platform. Subsequently, data conversion is facilitated through the proposed transformation interface. Python codes are programmed to realize automatic processing of numerical simulation and results are extracted to the ML algorithm for the prediction model. An optimization algorithm is implanted in the numerical stream file to retrieve the optimal relative intersection angle between the tunnel axis and the trend of rocks. A case study is conducted to evaluate the feasibility of the proposed framework. Results demonstrate a substantial improvement in design and optimization accuracy and efficiency. This framework holds immense potential to propel the intellectualization and informatization of underground engineering.