The aim of this project was to develop a detailed, modular numerical framework to simulate flow and heat transfer in a large-scale vertical farming unit (climate cell). We combined high-fidelity CFD with machine-learning acceleration (a fast surrogate model, trained on a judicious design of experiments from the CFD). An active-learning loop iteratively selected the most informative cases for additional CFD runs, reducing total compute while expanding scenario coverage. This hybrid approach preserved the governing physics (radiation, convection, buoyancy, moisture transport, turbulence and crop-bed porosity) while enabling much faster what-if analyses for design optimisation. The framework assessed the impact of supply temperature/humidity set-points, rack layouts, diffuser strategies, and lighting heat loads on chamber performance. Effects of heat transfer, velocity and pressure on the crops were captured qualitatively (contours, streamlines, vectors) and quantitatively (averaged values, uniformity indices, and surrogate-predicted response surfaces). For critical short-listed designs, CFD re-verification was used to confirm surrogate recommendations, with mesh/solver settings tuned for buoyant stratification and jet entrainment. The result is a scalable, ML-accelerated digital twin that supports rapid design space exploration, set-point scheduling, and energy–uniformity trade-offs. This provided the client with a robust tool for optimisation and operational planning. Vertical farming, including this state-of-the-art facility, plays an important role in achieving a sustainable, net-zero future and contributes significantly to food security. This work has continued in additional phases to scale up the climate cell.