The integration of Artificial Intelligence (AI) and Ambient Intelligence (AmI) has emerged as a promising approach to creating responsive and contextually aware environments. AmI creates contextually aware environments by seamlessly integrating intelligent technologies, while AI develops algorithms for autonomous learning and decision-making. However, embedding AI within AmI environments faces challenges due to limited resources and energy constraints. While recent research on embedded AI has primarily focused on specific tasks of AmI, our goal is to develop a comprehensive framework encompassing all the necessary components for practical use cases. Through this endeavor, we aim to explore power-aware designs and distributed learning as fundamental approaches to address limited computational resources, energy constraints, and dynamic context variations challenges.