With the growing adoption of online platforms for music education, ensuring data security and intelligent access control has become increasingly critical. This research proposes a Role-Based Access Control (RBAC)-based multi-level security architecture specifically designed for piano e-learning environments. The architecture assigns user-specific roles to students, instructors, and administrators to manage access privileges while safeguarding sensitive educational content and personal data. To enhance adaptability and intelligence, the Dynamic Transient Search-driven Multi-Kernel Support Vector Machine (DTS-MKSVM) model is employed to implement predictive access control and adaptive authentication. Data from 1693 users were collected through a piano e-learning platform, capturing various behavioral features for analysis. These features are normalized using a Z-score to standardize data and minimize outlier impact before forecasting access patterns. The risk-based authentication model also calculates the necessary degree of authentication and hence enhances security and the user experience. The proposed system is tested against a number of performance measures, such as accuracy (97.3%). According to experimental results on the simulated piano e-learning platform, the predictive access control model is characterized by high accuracy. The adaptive authentication component successfully identifies the usage of anonymity when logging in and produces a secure, intelligent, and user-friendly online piano learning environment that complies with privacy in terms of protection against misuse of educational data.