Slope failure is a significant risk in both civil and mining operations. This failure phenomenon is more likely to occur during the high rainfall season, areas with a high probability of seismic activity and in cold countries due to freezing-thawing. Further, a poor understanding of hydrogeology and geotechnical factors can contribute to erroneous engineering designs. Several Limit Equilibrium Methods (LEMs) and numerical modelling tools have been developed over the years. However, the highlighted success of the Artificial Neural Networks (ANNs) in other disciplines/sectors has motivated researchers to implement ANNs to forecast the Factor Of Safety (FOS). This paper aims to develop ANNs to predict the value of the FOS for slopes formed by (i) uniform one soil/rock material and (ii) formed by two soil/rock materials. Each of these slopes contains three sub-models with 6, 7 and 8 input material parameters. Thousands of FOS values were generated for each sub-model using LEMs by randomly generating material input parameters. Over 80% of generated FOS values were used to train ANNs and the remaining 20% were used to for validation. The one-material models performed better than the two-material models overall. The first sub-model from the one-material models and the third sub-model from the two-material models exhibited the best performance compared to the other sub-models, achieving Mean Square Error (MSE) of 8.35E-04 and 5.10E-3, respectively. The third sub-model from the one-material models and the first sub-model from the two-material models have a MSE of 2.00E-3 and 9.80E-3, respectively. The second sub-models have shown the lowest performance compared to the other models. The minimal errors between LEMs and ANNs have led to the conclusion that ANN can be used as a tool for a quick and first-pass analysis by design engineers without undertaking rigours, complex, time-consuming and tedious computation of FOS using LEMs. An actual field-tested database can be usedto predict real-world slope failures.