"Smart Embedded Systems: Advances and Applications" is a comprehensive guide that demystifies the complex world of embedded technology. The book journeys through a wide range of topics from healthcare to energy management, autonomous robotics, and wireless communication, showcasing the transformative potential of intelligent embedded systems in these fields. This concise volume introduces readers to innovative techniques and their practical applications, offers a comparative analysis of wireless protocols, and provides efficient resource allocation strategies in IoT-based ecosystems. With real-world examples and in-depth case studies, it serves as an invaluable resource for students and professionals seeking to harness the power of embedded technology to shape our digital future. The use of Machine Learning (ML) and Deep Learning (DL) techniques in healthcare has been widespread over recent decades. ML algorithms include multi-layer perceptron (MLP), artificial neural networks (ANN), support vector machine (SVM), multi-class SVM, random forest (RF), XGBoost, K-nearest neighbor (KNN), na?ve Bayes (NB), and extreme learning machine (ELM).