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A competent alternative approach to the conventional modelling techniques is artificial intelligence (AI). AI is about the computer science division that creates human intelligence machines and applications. AI-based solutions are great substitute where research is not feasible to evaluate engineering design parameters and therefore contribute to substantial human-time and effort savings in experiments. AI can also speed up the decision-making process, minimize errors and improve performance. AI can also make the decision-making process faster, reduce error rates and improve computing efficiency. Reinforced pile embankment is a useful technique used in soft soil construction, because of its advantages in reducing settlement, building time, and economic efficiency. The response of deep bottoms subjected to a uniform static surcharge was considered in most of the existing research. Very few researchers have studied the cyclical load effect or the effect of variation in depth. An experimental analysis was carried out on the impact of cyclical loading at a specific area of unreinforced and reinforced embankment of differing heights. During the initial stages of cyclical loading, soil arching, regardless of the height of the embankment, was adversely affected. However, the arching of the soil started to improve somewhat by increasing the number of cycles. The magnitude of the loads transferred to piles was increased by the reinforcement layers. But for a thinner embankment, the improvement was more obvious. The main purpose of this report is to identify the AI techniques that can be used to analyze the behaviors of pile foundation undergoing cyclic loading by making models which will help in testing different kinds of failure patterns and the solutions with which these problems can be mitigated.