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The indoor environment directly affects health and comfort as humans spend most of the day indoors. However, improperly controlled ventilation systems can expend unnecessary energy and increase health risks, while improved thermal and air quality can often result in higher energy consumption. One way to approach this dilemma is by understanding the effectiveness of the variables influencing indoor air quality (IAQ) related health and comfort. The objective of this paper is to highlight evidence and variables from empirical and deterministic models, which are combined in analytical models that current machine learning techniques often overlook. This paper reviews the analytical models and identifies the corresponding input variables, discussing their application in models based on artificial neural networks (ANNs) and reinforcement learning (RL). ANN and RL models have accurately described non-linear systems with uncertain dynamics and provided predictive and adaptive control strategies. The first part of this study focuses on the most common thermal comfort models and their variables, mainly related to steady-state and adaptive models. The second part reviews typical models of determining indoor air pollutants and their relationship with ventilation requirements and health effects. Forty-five works closely related to the field are summarized in multiple tables. The last part identifies the factors needed to predict thermal comfort and IAQ. • Analytical models and its variables of IAQ related thermal comfort are discussed. • Analytical models and its variables of IAQ related health are investigated. • ANNs have emerged as the most used technique to define building sensing systems. • RL is an efficient one for improving human-building interaction. • Improving thermal comfort and health while lessening energy use is feasible. [ABSTRACT FROM AUTHOR]