Computing environments necessitate the prerequisite of ensuring performance pinnacle and entailing resilience during disruptions. Most often, system faults can interrupt the overall productivity and prolong operational time, thereby increasing computational complexities, along with leading to downtime, mitigated productivity, and detriments in terms of finances. The inevitability to swiftly effectuate precise system failure detection, stratification and further resolution, becomes crucial for effectively maintaining system veracity and evade unethical injections. This indagation pivots on analyzing the various attributes relevant to system fault processing, and to entail the data thresholding combined with feature extraction to efficiently identify and classify system failures using machine learning algorithms. The proposed study entails a multi-modal real-time feature evaluation from the database constructed using the primary attributes such as the upstream connection, response time, API latency, connection time, and transaction status. Feature ranking using variance, Region of Curve (ROC) and T-Test are incorporated to enhance accuracy of classification. The machine learning algorithms used in this research paper are the Efficient Linear Support Vector Machine (ELSVM), Naïve Bayes algorithm and Tri-layered Neural Network, and the performance accuracy rendered by each of the algorithms are scrutinized. The simulation results are carried out in MATLAB, and the results are procured successfully.