Machine Learning Applications with Sensors for Indoor Air Quality Research.

Nowadays, people spend over 80% of their lives indoors, which makes indoor air quality (IAQ) research important. The paper presents, firstly, a structured overview of publicly available IAQ datasets suitable for machine learning (ML) research, secondly, a comparative analysis of the reviewed datasets, thirdly, an ML-oriented mapping between tasks and algorithms, to outline the algorithmic families that are most appropriate given the dataset structure and the prediction target, and fourthly, an investigation on IAQ-ML using custom-made solutions that include sensors for data acquisition. The methodology included an analysis of 1162 papers from the Web of Science, 1536 from Scopus, and 756 from IEEE Xplore, between 1 January 2020 and 31 December 2025, to capture recent trends in ML-based IAQ research. The findings show that linear regression (132 articles), Logistic regression (91), random forest-RF (77), Long Short-Term Memory-LSTM (77), Principal Component Analysis (63), and Elastic Net are the most popular among researchers. Most studies report accuracy over 90%, with maximum values of 99.37% for LSTM and 99.20% for RF. In the case of regression, the R