The Distribution Network Operators (DNOs) operate and plan the active distribution grids (ADGs), which are composed of tens of thousands of electric distribution substations and millions of end-users (consumers and prosumers). With the help of the smart metering infrastructure, the data collected from these points must be analysed by experts (Decision Makers—DMs) who know the structure and operating regimes of the ADGs very well. They rely on processing techniques and their experience and knowledge to make the best decisions. However, due to the chosen algorithms in the data processing, which use mathematical models built on some assumptions, DMs should justify their decisions based on the identified solutions and the technical and economic impact on the ADGs. The DMs began using Artificial Intelligence (AI) techniques as they looked to integrate them into their workflows. These can perform remarkably well in various processing tasks, which are in direct contrast to the capabilities of the DMs in these areas. Over the past few years, many AI algorithms have been developed to improve machine learning models’ explainability, which is a challenge that has prompted many researchers and developers to propose new approaches to improve this field. The Explainable Artificial Intelligence (XAI) concept has been implemented to help DMs understand how these AI models operate. The incorporation of AI techniques represents an additional task to human-computer interaction as it raises the possibility that XAI will significantly impact the cognitive and behavioral aspects of assisted decision-making. In the chapter, an XAI-based system proposed as a framework that aims to provide explanations of how AI models work is developed to identify the feasible solutions for the planning and operation issues due to the integration of the active end-users (prosumers) from the ADGs, such as the estimation of the energy efficiency through loss level. The identified solution through the novel proposed framework can help DNOs make profitable investments to save as much energy as possible by implementing effective strategies in areas with technical issues.