Cholera remains a significant public health challenge, particularly in regions with inadequate water and sanitation infrastructure. Predictive learning and advanced data analytics offer critical tools for anticipating outbreaks and enabling timely interventions. This article explores the implementation of predictive learning using Convolutional Neural Networks (CNNs) and MATLAB to predict cholera outbreaks. By leveraging historical data, including cholera incidence rates, meteorological conditions, and environmental factors, CNNs can recognize complex patterns that signal impending outbreaks. MATLAB provides a robust environment for data analysis, visualization, and deep learning model development. We detail the steps involved in data collection, preprocessing, CNN architecture design, training, and evaluation. A case study demonstrates the application of this approach in a high-risk region, highlighting its potential to improve predictive accuracy, optimize resource allocation, and enhance public health response. Despite challenges such as data quality and computational demands, the integration of CNNs in cholera prediction presents a promising direction for mitigating the impact of outbreaks and improving public health outcomes. Cholera prediction, Convolutional Neural Networks (CNNs), MATLAB, Predictive learning, Epidemiology, Public health interventions.