Intensified heat treatment, using direct contact condensation (DCC), is applied in the production of dairy products to ensure a high level of food safety. The key challenge with DCC is the fouling due to the protein reactions that limits operational efficiency and sustainability. Using a condensation regime map can improve operational decision‐making. Pilot plant scale experiments were conducted for a wide range of steam mass fluxes and inlet temperatures at high and low channel pressures. High‐speed images were recorded and analyzed to obtain penetration lengths and plume area. The experimental data and image analysis supplemented with temperature and pressure measurement, were processed using machine learning (ML) to develop a data driven model to predict the regime maps. The linear discriminant analysis (LDA) was found to be the most suitable model. From the ML models it was also found that the best parameters to make a condensation regime map are the steam pressure, channel pressure, subcooling temperature, water Prandtl number, and the relative velocity ratio between gas and liquid. The condensation outcomes were presented with various two‐dimensional regime maps. New regime maps are proposed using the Prandtl number and velocity ratio as dimensionless parameters.