Immersive IoT technologies for smart environments involving mobile edge computing is an effective solution for managing resource-intensive mobile applications by offloading computations to nearby servers, which reduces energy consumption on local devices. However, selecting the optimal tasks to offload considering data transfer and communication latency is complex. The chapter provides a detailed overview of the emerging technologies that are in practice for the smart environment realization. Additionally, this work introduces the energy-efficient machine learning-based offloading scheme that uses machine learning to identify CPU-intensive tasks based on factors like energy consumption, network conditions, and delays. We formulate a cost function to evaluate task offloading policies and train a machine learning model to optimize computations. Results show a remarkable 99.6% accuracy in device selection, significantly extending the lifespan of mobile devices.