In today’s world, everyone is preoccupied with work and other activities, leav- ing little time to visit doctors about illnesses that may appear to be minor at first but develop into life-threatening conditions as time passes. As a result, the proposed model accesses a public repository that maintains numerous symp- toms and their possible diseases as a matrix for early disease prediction and prevention. Symptoms are received from the user and fed into the embed- ded blending algorithm to estimate the type of disease. The patient’s records are collected from the several hospitals and the resulting massive volume of data, which results in inefficient prediction model using the machine learning approaches. Since the proposed model is a combined approach of training mechanism, it can reduce the number of accessing records in every step. Tra- ditional approaches like bagging and boosting construct more number of deci- sion trees because of the vast amount of data. This results in the utilization of more number of resources and sometimes CPU enters into saturation state. The proposed system solves this problem by using optimized parameters for tree construction and reduces the memory and resource utilizations.