Clustering algorithms play an important role in movie recommendation systems by allowing the grouping of similar users or movies together. This grouping facilitates more accurate and personalized recommendations. Clustering contributes to a better user experience by delivering more relevant and timely recommendations. Users are more likely to engage with recommendations that closely match their interests and needs. K-means clustering is a powerful algorithm in this context, facilitating personalized recommendations. This paper presents a novel movie recommendation system, Demographic-KM, which utilizes a customized K-means algorithm that leverages user demographic information. The proposed approach uses K-means clustering to segment users and movies into clusters and makes recommendations based on these clusters. It creates a user clustering system based on demographic information and a movie clustering system based on the genres. It maps user clusters to movie clusters using rating data and recommends movies to new users based on their predicted clusters. This association allows the recommendation system to suggest movies from the most suitable movie cluster for a new user, determined by their predicted user cluster. The clustering quality is evaluated using silhouette scores, root mean squared error (RMSE), and mean absolute error (MAE). The proposed method shows low MAE and RMSE values and high silhouette scores, indicating strong clustering performance as well as improved recommendation.