Emerging internet of things (IoT) technologies, combined with sensors and data analytics, enable intelligent management of buildings, significantly improving energy efficiency and optimizing thermal comfort for occupants. In this paper, we present a novel modeling framework that combines IoT approaches with multivariate statistical analysis (MVA) methods to measure indoor comfort and predict the optimal temperature (T) that maximizes occupant satisfaction. By continuously monitoring real-time environmental conditions, 11 indoor and outdoor environmental variables are collected every 20 minutes at multiple locations via an IoT network. In addition, surveys are conducted four times a day over a 21-day period to measure occupant satisfaction with indoor comfort. Both linear models, using standard regression, and nonlinear models, adopt locally weighted regression (LWR), which was developed for the first time in this context to predict indoor comfort using current and historical environmental data. The root mean square error prediction (RMSEP) is approximately 9.6% in predicting user satisfaction at the selected site. A model is also developed to predict the optimal T that maximizes occupant satisfaction, with a predicted satisfaction rate of 98.82%. In particular, the model demonstrates significant increases in occupant satisfaction after predicting the optimal T across three different rooms, with improvements from 5.18% to 100%, from 12.69% to 91.53%, and from 58.48% to 100%, respectively. This study highlights the significant role of IoT and MVA in uncovering intricate correlations within complex data sets. To our knowledge, this is the first time MVA has been applied to indoor wellness models, where neural networks are currently the most used approach with higher computational complexity. The proposed framework sets the stage for the optimum control of heating, ventilation and air conditioning (HVAC) systems in buildings, for example by using predicted optimal temperatures to reduce energy consumption through minimizing unnecessary HVAC operations while preserving thermal comfort.