Integrating machine learning algorithms into embedded systems requires balancing performance with limited hardware resources. Techniques such as model compression, quantization, and pruning help reduce computational complexity while maintaining accuracy. Lightweight frameworks like TensorFlow Lite and TinyML are specifically designed for deployment on microcontrollers and low-power devices. Efficient use of memory, hardware accelerators, and optimized coding practices further ensure smooth functioning. By applying these methods, embedded systems can perform intelligent tasks such as anomaly detection, speech recognition, or predictive maintenance without straining power and storage capacity. Continuous learning in this area can be enhanced with an embedded systems course.