Recent studies have witnessed the remarkable merits of metaheuristic algorithms in optimization problems. Due to the significance of early analysis of thermal load in energy-efficient buildings, this work is carried out to introduce and compare four novel optimizer techniques of firefly algorithm (FA), optics inspired optimization (OIO), shuffled complex evolution (SCE), and teaching-learning-based optimization (TLBO) for accurate prediction of heating load (HL). The models are applied to a multilayer perceptron (MLP) neural network to surmount its computational shortcomings. The models are fed by a literature-based dataset obtained for residential buildings. The results revealed that all used models are capable of properly analyzing and predicting the HL pattern. A comparison between them, however, showed that the TLBO-MLP with the co-efficients of determination 0.9610 vs. 0.9438, 0.9373, and 0.9556 (respectively for FA-MLP, OIO-MLP, and SCE-MLP) and also root mean square error of 2.1103 vs. 2.5456, 2.7099, and 2.2774 presents the most reliable approximation of the HL. It also surpassed several methods used in previous studies. Thus, the developed TLBO-MLP can a beneficial model for subsequent practical applications.