<p>The growing trend for adaptation of the Industry 4.0 concept has inspired different manufacturing sectors to include different intelligent machinery in their production and allied systems for sustainability improvement. IoT is a trending system, presently used in the different domains of the manufacturing sector and acts as a subset of intelligent machines. Thus, A minor fault in the system might be responsible for losing different resources. To overcome such circumstances, fault diagnosis is required for the said system. Parallelly, failure prognosis is also essential for the prediction of the future condition of the system. The root causes of system failure have been identified for both diagnosis and prognosis. The knowledge of system configuration is essential to identify the root cause of failure. Fault tree analysis (FTA) is a potential tool, that has been used to identify probable failures of the system. Sufficient data regarding the failure of the system components or Basic events (BE) have to be known in this regard. Hence, Fermatean-Fuzzy FTA (FFFTA) was conducted using experts’ elicitation to estimate the failure probability (FP) of the basic events (BEs) in the absence of requisite failure data. The FP over the different periods has been evaluated through this analysis. Based on the FP of the BEs, the effect of each event on system health has been evaluated in the form of posterior probability using the Bayesian network (BN). Lastly, a suitable prognostic model has been utilized for the Remaining useful life (RUL) prediction of the critical BEs, which has been selected based on posterior probability. An IoT-Based Intelligent weight data communication system has been considered as a case study in this work. The Auto-regressive integrated moving average (ARIMA) model has been chosen for RUL prediction. The Pareto principle has also been found supportive for the said system.</p>