We propose and design a machine learning based residential solar power management system that will monitor, manage and optimize power consumption in a residential home. Most off grid solar powered homes inherently face the challenge of limited power since solar power is optimum during day. Power stored is easily depleted at off peak hours. We seek to address the challenge of inefficient usage of power. The proposed system aims at efficiently controlling power consumption in a residential home by incorporating machine learning to aid in decision making based on how power is consumed in the home. The system will ensure effective usage of the available solar energy increasing reliability on solar energy. For the solar energy powered appliances to work at their optimum, it is necessary to ensure proper management of solar energy generated. This will impact the life span of the solar energy equipment as well as improve the lives of the people over time. This is because solar energy power is not an ideal energy source. The solar panels can generate power at certain times of the day, therefore the most important consideration for using the solar power is to maximize the utility of the solar power when its available. We thus propose a machine learning based SHS which helps mitigate the existing and also possible challenges in the SHS sector even as it grows. We aim at increasing the efficiency of energy use as the end user has more control of their system. We will make use of sensors as opposed to IoT since the target market may have unreliable internet connection or none. Our project incorporates an application that enables the user to know the State of Charge of the battery for providing the user with an indication of the capacity left in the battery. It will be able to make intelligent decisions automatically . This is found to be essential in optimizing the charging process in addition the load on the battery is easily monitored to ensure a longer battery shelf life and providing the required power for the needed time period. #powermanagement Master Controller Slave Controller