Predicting events based on available data is an effective way to protect human lives. Issuing health alert based on prediction of environmental pollution, executing timely evacuation of people from vulnerable areas based on prediction of natural disasters are the application areas of sensor data stream where accurate and timely prediction is crucial to safeguard people and assets. Thus, prediction accuracy plays a significant role to take precautionary measures and minimize the extent of damage. Belief rule-based Expert System (BRBES) is a rule-driven approach to perform accurate prediction based on knowledge base and inference engine. It outperforms other such knowledge-driven approaches, such as, fuzzy logic, Bayesian probability theory in terms of dealing with uncertainties. On the other hand, Deep Learning is a data-driven approach which belongs to Artificial Intelligence (AI) domain. Deep Learning discovers hidden data pattern by performing analytics on huge amount of data. Thus, Deep Learning is also an effective way to predict events based on available data, such as, historical data and sensor data streams. Integration of Deep Learning with BRBES can improve prediction accuracy further as one can address the inefficiency of the other to bring down error gap. We have taken air pollution prediction as the application area of our proposed integrated approach. Our combined approach has shown higher accuracy than relying only on BRBES and only on Deep Learning. / <p>This is a Master Thesis Report as part of degree requirement of Erasmus Mundus Joint Master Degree (EMJMD) in Pervasive Computing and Communications for Sustainable Development (PERCCOM).</p>