The importance of office occupancy detection is evident, due to its critical role in workplace management and facility optimization. Recent studies have recommended the use of data-mining techniques for this purpose. In this research, a powerful hybrid model is developed upon artificial neural network (ANN) coupled with shuffled complex evolution (SCE) algorithm for the binary prediction of occupancy in an office room based on several characteristics such as light, temperature, humidity, and CO2. The trained model is tested via two datasets (considering the opened/closed office door while occupied) and according to the results, it achieved an excellent prediction. In detail, the values of area under the curve (AUC) were above 0.98 along with mean square errors below 0.03; indicating the superiority of the proposed SCE algorithm compared to three baseline techniques, namely Archimedes optimization algorithm (AOA), incomprehensible but Intelligible-in-time logics (ILA), and gazelle optimization algorithm (GOA) in this study, as well as some others in earlier literature. Moreover, the SCE-ANN emerged as the most efficient (i.e., the quickest and least complex) algorithm. In the end, the dataset is exposed to a well-known statistical method called principal component analysis (PCA) to evaluate the importance of the considered input factors and identify the most crucial ones. Altogether, the findings of this study can provide valuable environmental and economic contributions to related sectors such as building energy management.