A recommender system has very important role nowadays, be it business, e-commerce, search engines, entertainment, etc. The need for faster, dynamic, and efficient recommender system arises with huge data on the Internet or website platform. A movie or anime recommender system has a major role in delivering improved entertainment. These recommender systems can provide much personalized recommendations, suggestion to a particular user based on one’s watching habits, or other similar user’s interests, ratings. Plentiful of recommendation techniques has been proposed but most are not able to provide useful recommendation within a very short span of time. In this paper, we aim to propose a fuzzy-clustering-based recommender system which is almost quite efficient and accurate as collaborative filtering (CF) technique but much faster than CF. We have achieved an improvement of approximately 4 s faster than CF techniques. Experimental data justifies the efficiency of the system. © 2020, Springer Nature Singapore Pte Ltd.