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Cloud computing platforms lie at the very heart of today’s mobile and web-based applications. Cloud service providers must satisfy computational performance agreed through service level agreements (SLA) and simultaneously keep their operational costs, clusters health, and other cloud parameters within acceptable ranges in order to achieve business success. Using traditionally available monitoring tools is not sufficient to understand in depth how these different factors affect each other. Therefore, intelligent systems able to predict operational parameters from the usage behavior of a cloud data center can potentially be beneficial. This project aims to develop an algorithmic approach that models the relationship between cloud usage parameters such as CPU and memory usage and the cloud cluster’s health parameters such as temperature. Neural network models are trained using data from different machines, and experimental results show that the models deliver promising results in terms of modeling machines’ health parameters using usage parameters.