Meeting the escalating necessity for transporting goods, specialized high-capacity railroad corridor has been developed to support the transportation of heavier loads by larger trains. This resulted in improved productivity and reduced unit costs. However, building high-capacity railway routes typically necessitates a significant financial investment, necessitating rigorous risk assessment during the original design stage. In this current study, slope stability analysis of the proposed high-capacity railroad corridor, which is 12.293 m high embankment, has been investigated using three distinct neural networks (NNs), such as the Convolutional Neural Network (CNN), the Deep Neural Network (DNN), and the Radial Basis Functional Neural Network (RBFNN). The study gave special consideration to a 12.293 m high railway embankment on the Indian Railway. For this purpose, 100 random datasets have been generated for soil parameters, and all employed networks has been coded in python programming language. Following the construction of the models, the developed NNs were mapped using TIC, R2, RRSE, RAE, and LMI during the training and testing phases. Based on the evaluation metrics, the proposed CNN found the best fit to the collected datasets in terms of R2 = 0.9998 and TIC = 0.0074 in testing phase. Furthermore, Tailor diagram has also been presented for training and testing phase.