Tiny Machine Learning (TinyML) enables machine learning on resource-constrained devices, such as microcontrollers, by optimizing algorithms for low power consumption and minimal memory usage. This approach bridges the gap between edge devices and AI, offering real-time, on-device processing without reliance on cloud infrastructure. The proposed TinyML architecture consists of four key stages: data collection and preprocessing, model development, deployment and inference, and monitoring and updates. Data preprocessing includes cleaning and bias detection to ensure quality input for model training. Model development focuses on selecting, training, and optimizing models, integrating security measures such as data encryption and privacy-preserving techniques. Deployment leverages edge device compatibility, allowing inference engines to process data locally. Continuous monitoring and retraining mechanisms ensure performance consistency and adaptability to new data patterns. By combining efficiency, privacy, and scalability, this architecture addresses challenges in deploying AI for IoT and low-power applications. TinyML holds immense potential to democratize AI by enabling intelligent applications on ubiquitous, low-cost devices.