With raising energy cost, environmental concerns, and the widespread use of the smart home technologies, efficient energy management in residential buildings is becoming increasingly important. In this thesis, I present an end to end design for a Smart Home Energy Management System (SHEMS) that utilises the ZWave sensors and the smart devices with the Home Assistant on a Raspberry Pi. Reinforcement Learning (RL) is used in the system to autonomously optimize energy consumption and comfort level by using the algorithm of Proximal Policy Optimization (PPO). The agent was trained on a simulated environment, with realistic home scenarios that bypass practical limitations due to hardware. The model was tested thoroughly including a large number of smoke tests and more than 80,000 timesteps of training. Visualizations in TensorBoard provided good insight into how the agent was learning and how effective the actions it made were. This paper demonstrates that the RL based SHEMS not only achieves excellent tradeoff between energy efficiency and user comfort but also achieve super energy conservation compared to traditional management. The findings complement the possibilities of utilizing advanced machine learning methods on top of residential home automation frameworks to make valuable contributions and operational concepts for an environmentally friendly and intelligent energy operation in residences.