The application of Machine Learning (ML) and Deep Learning (DL) techniques within Computational Fluid Dynamics (CFD) is increasingly moving from a frontier research topic to a concrete and practical approach. Traditional numerical methods in CFD are often hindered by high computational costs and long simulation times. ML and DL methods offer viable alternatives that not only serve as substitutes but can also enhance classical CFD approaches, addressing inherent challenges such as excessive computational demands and extended processing durations. In industrial settings, these advancements are critical for improving the efficiency and sustainability of aerodynamic, thermal, and fluid-structure interaction studies. This thesis focuses on the development of a cloud-based ML framework for CFD, designed and implemented from the ground up within the Databricks environment in collaboration with Stellantis.