<![CDATA[HVAC (Heating, Ventilation, and Air Conditioning) systems play a crucial role in maintaining thermal comfort, air quality, and energy efficiency in buildings. This study aims to examine the contribution of Fourier series in HVAC thermal analysis and the integration of artificial intelligence (AI) and smart sensors to support energy optimization.The method used was a Systematic Literature Review (SLR) of scientific publications from 2019 to 2025 obtained from the ScienceDirect, IEEE, MDPI, and Academia databases. Articles that met the inclusion criteria were analyzed based on their objectives, methodologies, and research results, then grouped thematically. The results of the study showed that Fourier series are effective in representing periodic temperature signals, simplifying data complexity, and improving thermal prediction accuracy. Meanwhile, the integration of AI and smart sensors enables real-time responses, improved energy efficiency, and thermal comfort stability. The main challenges identified include limitations in experimental validation, the complexity of integrating Fourier–AI–sensors into a single framework, and the high computational requirements for large-scale implementation. Thus, it can be concluded that this study contributes to mapping current approaches and recommending research directions for the development of more efficient, adaptive, and sustainable data-driven and intelligent computing-based HVAC systems.Keywords: Artificial Intelligence (AI), Energy Efficiency, Fourier Series, HVAC, Smart Sensors]]>
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