Aspiring researchers have to consider choosing an appropriate Ph.D. subject. However, the complexity of the regulations and the large number of possible choices especially in context of cross and multi-disciplinary approach render it challenging. The manual processing of applications by universities is time-consuming and prone to errors, which leads to inefficiencies and in-ordinate delays. We created DSPredict, a novel approach that employs machine learning to identify the most appropriate Ph.D. subject for each applicant. Our methodology assesses application profiles and predicts the most suitable subjects. The findings suggest that DSPredict surpasses traditional methods, resulting in increased accuracy and significantly shorter time to identify appropriate subjects.