This paper examined the application of Machine Learning techniques for famine prediction. Early detection of famine reduces vulnerability of the society at risk. The dataset used in the study was collected between 2004 to 2005 across households in the different regions of Uganda. Dataset from the northern region was found to be most suitable to training datasets of other regions. Classification performance of four methods as Support Vector Machine, K- Nearest Neighbours, Naïve Bayes and Decision tree in prediction of famine were evaluated. Support Vector Machine and K- Nearest Neighbours performed better than the rest of the methods however Support Vector Machine produced the best ROC which can be used by policy makers to determine the cut-off for determining famine prone households. It is recommended in this study that satellite data could be used in combination to show the relationship in prediction of food security as this may increase the specificity of those households at risk.