Zambia has been faced with a relentless public health crisis since 1977, that crisis being the water borne disease known as cholera. The recent one being from October 2023 to mid 2024 with over 10, 887 cases and 432 confirmed deaths, and this was the most severe outbreak ever recorded in the nation's history. The main causes being the nation’s lack of a robust surveillance and reporting systems, as well as the absence of a system that can analyze historical data and environmental factors(like rain and temperature) all these things contributed to the slow detection and delayed response to the new cases, allowing the outbreaks to grow. This was mainly due to the poor communication between the public communities and the national health bodies. That’s where this proposed system, Random Forest(RF) and Long Short Term Memory Model (LSTM). It is, an AI-driven, community platform that is integrated with real-time surveillance, predictive analysis, Geospatial mapping, community reporting, machine learning algorithms(Random Forest and LSTM) and historical data using a dataset(YEM-CHOLERA-EOC-DIS-WEEK-20160424- 20200621.csv) to control the outbreaks before the become national outbreaks.