An understanding and analysis of student’s study habits is important for improving the educational outcomes and personalization of learning. In this review paper, we explore the research efforts to analyze/predict students’ study habits using machine learning (ML) and deep learning (DL) methods and the aim to study the trends, tendencies, and patterns in students’ study habits to enhance their performance and help them in developing healthy studying behaviors. The paper discusses recent ML and DL algorithms considering supervised, unsupervised, and reinforcement learning MD techniques in understanding students’ behavior and preferences. We explore natural language processing (NLP) which is widely used for analysis of textual data, computer vision to detect study environments, and time-series models to monitor study schedules. We provide a systematic overview of existing methodologies, datasets, and tools, as well as their possible shortcomings and the issues yet to be faced in the domain. Focus is paid on the ethical aspects, data privacy issues, and interpretability requirements for trust in artificial intelligence (AI) solutions in education. The results indicate that hybrid methods following the use of ML as well as DL technologies surpass standalone methods in terms of performance when identifying patterns in research habits to generate actionable insights for educators and learners. Through its analysis and discussions, this paper strives to be a roadmap for future studies that would bridge the gap between educational research and technological innovation for more effective, equitable, and data-driven education systems where future strategies and platforms offer tools and principles.