Tiny Machine Learning (TinyML) is an emerging field that combines embedded systems and machine learning, with a focus on empowering resource-constrained devices such as smartphones, microcontrollers (MCUs), and sensors. By enabling on-device machine learning applications, TinyML eliminates the need for cloud services and external power sources. This review provides a comprehensive overview of TinyML, discussing its definition, benefits, and challenges. It explores techniques like model quantization, pruning, clustering, and cascading architectures that make TinyML feasible. The review also emphasizes the importance of hardware-software co-design and presents methodologies and tools for implementing TinyML. Recent advances in TinyML across various domains, including image processing, natural language processing, and robotics, are discussed, highlighting the potential impact on embedded system capabilities and performance. The transformative potential of TinyML is explored, uncovering new possibilities for previously unattainable use cases and applications.