The heterogeneous data coming from varied sources is integrated to induce learning as it may have complementary information. The growing research for multi-source heterogeneous data analysis has extended the traditional methods of single source homogeneous data. One of the major challenges is processing the heterogeneous data while integrating the information from different sources with various missing data patterns. The potential features act as the useful predictors, but rejecting the observations with missing data values will lose the information which may create biased conclusions affecting the model strength for uniform decisions. A general imputation technique should be preferred for its computational simplicity and capability to induce a diminutive bias in the dataset. The proposed 'Heterogeneous Data and Weight Algorithm' constructs the robust model averaging techniques and feasible estimations with advanced Machine Learning techniques that implements the weighted probability approach to enhance the performance of the model with better predictive power.