Alumni are vital to a university's success, connecting the institution with the outside world. However, traditional alumni systems often fall short in fostering strong engagement, especially when it comes to mentoring current students. This paper introduces a Smart Alumni System (SAS) that combines social networking with machine learning (ML) and natural language processing (NLP) to enhance interactions between alumni, students, and faculty. This system supports a variety of activities such as mentoring, professional networking, group formation, and curriculum development. The ML algorithms provide personalized mentoring by matching people based on shared interests and skills, and NLP tools are used to analyze the student feedback towards the mentoring sessions. The existing system is a web application with networking features like connecting with friends, creating groups, and real-time messaging. By using ML and NLP in our system, it offers tailored recommendations, improves alumni relations, and makes mentoring more personalized and effective.