Procedural music generation in video game is rarely used in development despite its potential support for interactive nar- ratives, and falls behind broadly-used procedural methods like graphic management. Currently, developers rely on pre- produced audio sequences, focusing on sound quality while balancing storage and variety. Techniques like layering and re-sequencing are standard through sound design platforms like FMOD and Wwise, allowing adaptation and interactiv- ity in gameplay. However, this can lead to repetitive audio, which becomes particularly noticeable in extended gaming sessions, diminishing the impact of musical storytelling. This study introduces the Progressive Adaptive Music Gen- erator (PAMG), an algorithmic system that generates contin- uous music streams based on gameplay variables, seamlessly transitioning between moods, styles, and tension levels. It includes motivation, theoretical framework in the field of generative music, overview of the system structure, and a preliminary implementation test involving a Trial Game that compares conventional implementation with PAMG. In the discussion section, it addresses test results and issues, and fu- ture work. The study reveals a preference for PAMG’s music over traditional methods in certain aspects, although results are not conclusive.