Accurate simulation and analysis of quantum transport phenomena are essential for designing next-generation nanoscale devices in communication and computing. A hybrid computational framework is developed to simulate Gaussian wave packet dynamics using the Crank–Nicolson method, integrated with automationAutomation pipelines and AI-based modeling. Quantum interactions with single barriers, double barriers, and potential wellsPotential wells are explored in detail, revealing transmission behaviors consistent with analytical predictions. Resonance conditions are identified through automated signal processing, and surrogate models trained via machine learningMachine learning enable fast, accurate prediction of transmission coefficients across varying geometries and energies. Numerical results demonstrate excellent agreement with theoretical models, with deviations below 3%, validating the approach. The intelligent system supports inverse design and classification of quantum behaviors, offering practical tools for engineering resonant tunneling diodes, quantum logic filters, and photonic structures. By combining applied numerical physics with AI and automationAutomation, this work exemplifies intelligent solutions for quantum system design