Prediction of missing associations can be viewed as one of the most fundamental problems in the machine learning. The main objective of prediction of missing associations is to determine decisions for the missing associations. In real world problems, prediction of missing associations is must because absence of associations in the attribute values may have information to predict the decision for entrepreneurs. Based on decision theory, in the past many mathematical models such as naïve Bayes structure, human composed network structure, Bayesian network modeling etc. were developed. However, these theories have certain limitations. In order to overcome the limitations, rough computing is hybridized with Bayesian classification. This chapter discusses various techniques for predicting missing associations to obtain meaningful decision from information system. A real life example is provided to show the viability of the proposed research.