Indoor Environment Quality (IEQ) holds significant importance in building design and operation, and the simulation of indoor environments playing a crucial role in enhancing IEQ. Although Computational Fluid Dynamics (CFD) has been widely employed for simulating building environments, it is computationally demanding, particularly for large spaces. To tackle this challenge, we conducted a systematic evaluation of three surrogate models for accelerating CFD: Proper Orthogonal Decomposition (POD), Artificial Neural Networks (ANN), and a combined POD-ANN approach. Our evaluation criteria focused on assessing the model accuracy, the model size, computational time and extrapolation ability. A validated CFD case model and a real campus building are employed for model evaluation. The findings demonstrate that the top five modes can reconstruct the original data matrix accurately, and the POD-ANN significantly reduces model complexity and computation time by reducing the number of parameters in the neural network, the POD-ANN parameters is only 0.14 % of the ANN, and computation time is reduced by 63 %. In addition, the combination with ANN helps increase the extrapolation ability of POD significantly. In conclusion, this research proves that the POD-ANN can enhance the efficiency of CFD calculations with the advantages of both ANN and POD. By applying the POD-ANN to predict indoor temperature, we achieve faster predictions without compromising model accuracy, and an excellent extrapolation ability is achieved. This approach also reduces model complexity, highlighting its practical value for indoor environment prediction, particularly for large and complicated spaces.