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Most of the educational institutes and entities have an urgent desire to predict and measure student performance. This prediction helps them in assuring student retention, facilitating learning experience with necessary resources and increasing the university reputation and ranking. These requirements can be considered and Educational Data Mining (EDM) can be used to discover student learning environments with college context to estimate the student performance.¬† The educational institutions are curious to predict the student failure every time and this can be addressed by prediction method referred as Decision Support System (DSS) in a given arrangements. This DSS Prediction method used doesnt provide an accurate measure of the student failures since it lack the details of the parameters influencing the achievements of the students in a specific source in college context. Many researchers algorithm addresses only the classification and didnt provide any solution for the data mining issues such as data pre-processing and classification error, etc. The proposed prediction algorithm addresses the student performance extracting the specific keys and uses deep learning techniques. A classifier based on Deep Convolutional Neural Network (DCNN) predicts the student performance better compared to other classifiers. Three types of regularization methods are considered to compromise the problem of DCNN overfitting and thereby attaining early convergence such as weight regularization via the Genetic Algorithm (GA), Batch normalization and dropout. In this work, a new classifier is proposed namely Adaptive Weight Deep Convolutional Neural Network (AWDCNN) optimized by the use of GA algorithm to predict the performance of the students. The DCNN classifier weights are estimated using GA algorithm and these optimized weights are used to better classify the student results and the corresponding metrics used for performance analysis. The proposed classifier AWDCNN exhibited better student result prediction than other existing prediction methods and it is proved using training datasets in simulation results.