Abstract: Spectrum sensing and its efficient utilization are the main intriguing problems in Cognitive radio networks. Under-utilized spectrum creates opportunities to further investigate performance improvement through cognitive radio techniques. If the current spectrum is reclaimed by the primary user then the secondary user must vacate and switch to other available spectrum. Objective of secondary user is to maximize the probability of finding and utilizing an available channel while minimizing the investigation cost. The problem is proved to be NP-hard and a sufficient condition for a robust channel assignment need to be derived. In this study, we propose a “Multi-Channel Opportunistic Spectrum Analytics and Adaptive Channel Assignment (MC-OSACA)” technique, key objective of which is to maximize deliverable throughput through optimal spectrum sharing among cognitive users. The proposed approach strives to achieve a balance by minimizing interference to licensed users and maximizing the entire system performance providing opportunistic access to number of secondary users. It performs two major activities such as opportunistic spectrum sensing and adaptive channel assignment. First, spectrum analaytics using unbiased estimator is applied to find out an optimum list of idle channels. Second, these idle channel are fed to the next level estimator to predict the most appropriate channel for dynamic utilization in an adaptive environment for cognitive users. Moreover, we derive its performance bound on channel estimation and assignment through mathematical analysis. Prototype was developed to demonstrate the proof of concept and analyze the feasibility and practicality of using MC-OSACA technique in cognitive radio network. Simulation results show that our solution achieves better performance when compared to existing channel assignment approaches substantially satisfying the robustness constraints.