Korean dramas have quickly gained global recognition and attraction due to their diverse genres, story patterns, digital media platforms, and Web streaming technology. User ratings and opinions on these websites provide a lot of information. With these reviews, a system for recommending Korean dramas can be created to help viewers worldwide find the right shows and films that suit their interests. This paper aims to promote similar K-drama to viewers and display favorable or negative ratings. The study aims to deploy a Korean drama recommendation system with sentiment analysis. The recommendation system considers key factors to identify related dramas. In contrast, Sentiment Analysis evaluates user reviews to determine their opinions and emotions [1]. Using this technique helps determine how most users feel about a drama. The Korean Drama Recommendation System employs Cosine Similarity, Sentiment Analysis, and Support Vector Machine Classifier. The proposed sentiment analysis model achieves a maximum accuracy of 92%. This shows the model's accuracy and practicality in identifying reviews. It successfully gives consumers the top ten Korean dramas that match their tastes generated by the recommendation system.