Introduces a novel computational approach for efficiently navigating infinite multi-dimensional spaces using a bellande step function within a model-integrated dimensional framework. By optimizing the step function, the method addresses challenges in high-dimensional path-finding and node calculation, allowing movement towards target nodes within defined distance constraints. Leveraging this approach, efficiently compute the next step towards a target node, ensuring accurate movement while adhering to specified distance limits. The integration of dimensional space modeling enhances the path-finding process, demonstrating improved computational efficiency and accuracy. The results underscore the robustness and scalability of this approach, showcasing its potential applications in robotics, path-finding, and complex systems modeling. This integration of the step function with model-integrated dimensional space represents a significant advancement in the computational efficiency and precision of multi-dimensional node calculations.