This paper presents a conceptual framework for Student Relationship Management with AI Self-supervised Learning (SRM with AI SSL). The proposed Student Relationship Management with AI Self-supervised Learning framework includes several core components: SRM components, SRM process, AI Self-supervised Learning process. By integrating advanced technologies such as artificial intelligence and machine learning, the SRM with AI SSL can offer real-time insights and predictive analytics to identify at-risk students and provide timely interventions. AI Self-supervised learning, a subset of machine learning, leverages unlabeled data to train models, thereby reducing the dependency on extensive labeled datasets. This approach is particularly advantageous in educational settings where labeled data may be scarce or costly to obtain. By employing AI self-supervised learning, the SRM can identify patterns and insights from vast amounts of student data, including academic performance, attendance, and interaction with educational resources. The framework leverages integrating AI Self-supervised Learning (SSL) to analyze vast amounts of unlabeled student data, identifying patterns and insights. The findings suggest using AI Self-supervised Learning in SRM can transform the way institutions interact with and support students. It emphasizes the interplay between AI process and tools, SRM components, and SRM process to improve the quality of student support services, ultimately contributing to better educational outcomes.