<![CDATA[The Internet of Things (IoT) is growing rapidly, making it even more crucial to deploy Machine Learning (ML) models directly on edge devices with limited resources. TinyML fixes this matter by giving microcontroller-class hardware the ability to think for itself. This makes it less reliant on the cloud and better for latency, energy efficiency, and data privacy. This study offers a comprehensive Systematic Literature Review (SLR) of TinyML research published between 2021 and 2025, in accordance with PRISMA principles. We identified 429 records, removed 326 duplicates, and added 83 studies to the final synthesis. The evaluation examines five research inquiries concerning optimization techniques, streamlined architectures, sophisticated learning frameworks, application sectors, and hardware ecosystems. The findings underscore four key themes: enhancing models, utilizing specialized tools and technology, and adapting strategies. Some of the challenges that keep recurring are broken ecosystems, different benchmarking approaches, and on-device learning that isn't compelling when ideas shift. This research presents an open-access taxonomy that categorizes optimization techniques, application trends, and hardware constraints, thereby laying the foundation for a TinyML research agenda within the informatics community. Future directions highlight the importance of adaptive TinyMLOps pipelines, federated learning, LLM-assisted model design, and NVM‑based computing to support scalable and sustainable edge intelligence. The results underscore the relevance of TinyML for advancing informatics and computer science, particularly in enabling secure, efficient, and environmentally aligned IoT systems that support SDG 9 and SDG 12.]]>
