Content interest forwarding is a prominent research area in Information Centric Network (ICN). An efficient forwarding strategy can significantly enhance the user level performance parameters such as data retrieval latency. It also helps to minimize the origin server load, network congestion and overhead. Reinforcement learning is widely accepted for taking efficient routing decisions in network. This paper introduces a Q-learning driven forwarding strategy in ICN for interest packets. We have investigated the feasibility of exploiting reinforcement learning mechanism named Q-Learning for Named Data Network (NDN) paradigm of ICN. By revising Q-Learning mechanism to address the inherent challenges related to overhead and latency for content retrieval, this paper introduces design and implementation of Q-Learning based forwarding mechanism. It aims to gain learning through historical events and selects best mechanism to forward interest. The performance investigation of proposed protocol is carried out using simulator named ndnSIM-2.0. Outcomes are compared by integrating proposed protocol with LCD (Leave Copy Down), LCE (Leave Copy Everywhere, CL4M (Cache Less for More) and ProbCache (Probability driven caching). Protocol behaviour is also compared against recent routing and forwarding mechanisms. The considered performance parameters are data retrieval delay, server hit rate, network overhead, network throughput and network load. The experimental outcomes conclude that integration of proposed forwarding strategy to state-of-the-art protocols lead to performance enhancement up to 10–35%. The integrated protocol variants are also sensitive to any changes in network compared to existing approaches.