Formal predictive analysis remains limited in tobacco-dependent economies, where forecasting has largely relied on ARIMA-type time-series models. While widely used, these models impose linearity assumptions that restrict their ability to capture key structural drivers of production. This limitation is evident in recent studies where an ARIMA (1,1,0) model projected Zimbabwean tobacco yield at 1,511.78 kg/ha for 2023, underestimating the observed yield of 2,278 kg/ha by approximately 50.7% [1]. Export forecasting is even less developed, with most existing studies remaining descriptive rather than predictive. The paper reviewed the literature related to tobacco yield forecasting, agricultural export modelling and the application of Machine Learning in crop prediction and trade intelligence systems. Data from across the fields confirm that the Machine Learning techniques Ridge Regression, Random Forest and Gradient Boosting offer superior results to statistical models. The analysis points out three main gaps. First, Machine Learning methods have not been widely applied to tobacco production in sub-Saharan Africa. Second, there is no formal export forecasting model for Zimbabwe that accounts for its multi-year shipment patterns. Third, there is no integrated framework that jointly modelsyield and exports within a unified decision-support system. These gaps highlight the need for more comprehensive, data-driven approaches to forecasting in the tobacco sector.