A career is crucial for anyone to fulfill their desires through hard work. During their studies, students cannot find the best career suggestions unless they receive meaningful guidance tailored to their skills. Therefore, we developed an AI-assisted model for early prediction to provide better career suggestions. Although the task is difficult, proper guidance can make it easier. Effective career guidance requires understanding a student's academic skills, interests, extracurricular activities, internships, courses or training, research background, and skill-related activities. In this research, we gathered key data from Computer Science (CS) and Software Engineering (SWE) students to train machine learning (ML) and neural network (NN) models for career path prediction based on career-related information. To adequately train the models, we applied Natural Language Processing (NLP) techniques and completed dataset pre-processing. For comparative analysis, we utilized multiple classification ML algorithms and deep learning (DL) algorithms. This study contributes valuable insights to educational advising by providing specific career suggestions based on the unique features of CS and SWE students. The research also helps individual CS and SWE students find suitable industrial roles, research fields, and higher study fields that match their skills, interests, and skill-related activities. Furthermore, we developed an AI-driven career prediction website system, transforming how students receive career information and ensuring they make educated decisions about their future.