This paper presents L-M-6, an innovative algorithm designed to provide statistically accurate and democratically correct movie ratings using AI. Traditional movie rating systems often fail to capture the multifaceted opinions of viewers. In contrast, L-M-6 leverages natural language processing and machine learning to analyze user reviews and extract sentiments across seven key aspects of filmmaking: cinematography, direction, story, unique concept, production design, characters, and emotions. To enhance the accuracy and relevance of the ratings, a user survey is conducted to rank these aspects based on their perceived importance. The collected data is used to assign weights to each aspect, ensuring that the most valued elements have a greater influence on the overall rating. This weighted sentiment analysis provides a more nuanced and precise rating system. Moreover, L-M-6 continuously updates scores with new reviews using a rolling mean, ensuring that the ratings remain current and reflective of audience opinions. The algorithm’s ability to dynamically adjust and accurately represent diverse viewer sentiments makes it a significant advancement over traditional rating systems. Our results demonstrate that L-M-6 offers a more comprehensive and democratic approach to movie rating, aligning closely with audience preferences and enhancing the overall reliability of movie evaluations.