Outliers are considered as unexpected things observed while analyzing the data. Investigators found that the prediction and identification of outliers are extremely complex. Generally, a stream is measured as an unbounded data source executed promptly, and this research provides a novel way of predicting the outliers over the incoming data. Here, the incoming data is acquired from the hospital to validate the patients’ records. There are higher chances of outliers over the incoming data based on the density of the arrival data. The execution of outlier prediction is performed independently with the integration of LSTM (Long Short Term Memory) over the stacked CNN model. The outlier detection process constantly measures the incoming data from the emotional input as an outlier or inlier. Here, the data reconstruction is achieved with the auto-Regressive model, and the prediction model considers the outliers to construct the training data. Various hidden representation acquired from the stacked model is considered for outlier prediction, and the experimental results demonstrate that the anticipated model shows superior prediction accuracy, specificity, sensitivity and F1 score. The proposed model is best-suited for prediction as the deep learning approaches perform well over complex applications and acquire superior results. The simulation is done in MATLAB 2016b, and the performance metrics show a better trade-off than the other approaches.