This research focusses on Zakat and how inefficiency in the process of Zakat collection and distribution impacts poverty. This research studies how the problem of Zakat management is handled in various parts of the world as well as takes a deep look into previous research and proposed solutions in order to come up with a system that attempts to improve efficiency and transparency of the process while building on previous research in the area. The researcher utilized the Agile methodology using a scrum approach to develop the system. The system included a front-facing rule-based calculator to improve the zakat collection process and a machine learning API, built using the K-Nearest Neighbors algorithm, to improve the efficiency of zakat distribution. As such, the model was built using the K- Nearest Neighbors algorithm as it outperformed the other common classification algorithm such as Decision Trees, Naïve Bayes and Support Vector Machine. This process leveraged existing libraries and tools in both the Python and the JavaScript ecosystem. This research concludes that inefficiency in the zakat process could be improved by systemizing the whole process and suggests the developed system as a starting point.