Recent advancements in machine learning and computational chemistry have led to significant progress across various fields including heterogeneous catalysis, perovskite materials, and semiconductor fabrication. Researchers have developed multiscale computational frameworks for operando conditions in catalysis and applied deep learning architectures such as convolutional neural networks and transformers to model nonradiative recombination in CsPbI3 and Ge-doped perovskites. Machine learning techniques have also accelerated the discovery of low-k dielectric materials and enhanced the prediction of reaction conditions and drug-target interactions. In semiconductor fabrication, quantum machine learning has improved modeling of Ohmic contact resistance, outperforming classical algorithms in small-sample, high-dimensional scenarios. Breakthroughs in quantum circuits were achieved using magnetic graphene at room temperature, while new classes of magnons, quasiparticles relevant to spintronics, were directly observed, paving the way for high-speed spintronic devices. Additional innovations include the design of copper-based heterogeneous catalysts for CO2 conversion to methanol using extreme gradient boosting, and AI frameworks for optimizing synthesis sequences and reaction conditions in autonomous laboratories. These developments highlight the integration of machine learning with molecular simulations, materials science, and quantum technologies to drive forward research and applications in chemistry and physics.