Thermal Computational Fluid Dynamics (CFD) models analyze airflow and heat distribution in data centers, but their complex computations hinder efficient energy-saving optimizations for sustainability. We introduce a new method to acquire data and model 3D Convolutional Neural Network (CNN) based surrogates for CFDs, which predict a data center's temperature distribution based on server workload, HVAC airflow rate, and temperature set points. The surrogate model's predictions are highly accurate, with a mean absolute error of 0.31°C compared to CFD-based ground truth temperatures. The surrogate model is three orders of magnitude faster than CFDs in generating the temperature maps for similar-sized data centers, enabling real-time applications. It helps to quickly identify and reduce temperature hot spots(7.7