The idea of this article is to provide a framework for an exploratory analysis towards better comprehending the implications of AI-generated music on intellectual property rights, with a specific focus on copyright laws. The paper delves into the complexities surrounding the legal framework for AI-generated music, addressing challenges related to authorship, ownership rights, and the use of copyrighted material in training AI programs. To address the legal complexities associated with AI-generated music, the study proposes thirteen enablers based on a comprehensive literature review. Employing Interpretive Structural Modelling (ISM), the article establishes a hierarchical structure and interconnections among these enablers. The ISM model aims to identify critical factors that facilitate the integration of copyright laws into AI-generated music, providing insights for decision-making and strategic planning. The results and discussions section presents the final ISM model, illustrating driving and dependent enablers in the implementation of copyright laws for AI-generated music. Decisive factors such as legislative intent, robust infrastructure, and legal expertise are identified as driving enablers, while others, including "AI ethics and policy" and "common industry standards," depend on these driving forces. The study attempts to emphasize the need to focus on changing perceptions and attitudes toward AI technologies for the effective integration of copyright laws in AI-generated music. The study further develops a framework for understanding how the efficacy of ISM could be limited and compromised by various potential factors if not properly addressed.