Microelectromechanical systems (MEMS) have remained an innovative field since their introduction. Its applications have been observed in most general-use areas of human life, and for this reason, various advancements are continuously proposed for it. MEMS started with fabricating individual sensors to measure a particular response of the system, gradually shifted to integrating multiple sensors to record multiple responses, and has now moved to multiple devices with integrated sensors connected at a common point, the Internet of Things, where the devices can act smart. One additional requirement from the present perspective is to propose self-sustainable devices, which can generate power for themselves and are well capable of sustaining their lifetime and performance. Hence, artificial intelligence (AI) integrated smart and self-sustained sensors are the present interest. In this context, this review briefly discusses the basics of MEMS and machine learning (ML) and their integration to observe different applications of AI/ML in MEMS sensors in different fields. The various sensitivity and sustainability requirements in MEMS are primarily being catered to through proposing innovation in fabrication techniques, materials, detection schemes, and related hardware/software, which have also been briefed in the review.
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