Predicting crop disease outbreaks remains a significant challenge in agricultural management, with far-reaching implications for food security, yield optimization and environmental sustainability. Traditional disease surveillance systems largely rely on manual field inspections and reactive control measures, which are often labour-intensive and costly prone to human error. To overcome these limitations, this study proposes a machine learning based framework for forecasting crop disease outbreaks by integrating weather and soil data, thereby enabling risk-driven crop protection strategies. The study first investigates the epidemiological relationships between key environmental factors such as temperature, humidity, rainfall, and soil pH, as the occurrence of major fungal, bacterial and viral crop diseases. Using historical datasets obtained from agricultural extension services and meteorological stations, multiple predictive models are developed and evaluated, including Random Forests, Gradient Boosting Machines (GBMs), and Long Short-Term Memory (LSTM) neural networks. These models are assessed based on their ability to provide early warnings of disease outbreaks at the farm level, supporting proactive pesticide application and timely agronomic decision-making.