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@article{203142,
        author = {Mujtaba Ali Khan and Juveria Fatima},
        title = {A Comprehensive Survey of Machine Learning: Foundations, Challenges, and Cross-Domain Applications in Cybersecurity, Healthcare, and Education},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11675-11687},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203142},
        abstract = {Machine Learning (ML) has become a key technology that underpins data-driven decision making in almost all fields of industry and society. But, the real-world implementation of ML models faces a number of ongoing issues with data quality, privacy, security, compliance, scale, and interpretability. In this paper, the authors provide an extensive survey of ML methods, modeling paradigms, and their cross-domain applications, focusing on three areas of great impact: cybersecurity, healthcare, and education. We use a structured review protocol that follows the PRISMA guidelines, examining the literature that has been published in peer-reviewed journals from 2015–2025, and synthesize results based on an overall taxonomy, mapping the different ML paradigms (supervised, unsupervised, semi-supervised, reinforcement, deep, online, federated, and generative learning) to representative model families and domain-specific applications. We perform comparisons with the performance of the models, explore the use of generative AI and large language models (LLMs), and address related issues such as data privacy, fairness, explainability, robust models to withstand adversarial attacks, and energy sustainability. We found that while ML has shown to have transformative abilities in threat detection, clinical diagnostics, and adaptive education, it still needs to be implemented in a responsible manner, with interdisciplinary frameworks that combine privacy-preserving computations (including federated learning, differential privacy, etc.), explainable AI (XAI) and strict data governance. Finally, we discuss open research questions and future directions towards trustworthy, scalable, and fair ML systems.},
        keywords = {Machine learning, Deep learning, Cybersecurity, Healthcare AI, Education technology, Federated learning, Large Language Models, Explainable AI, Adversarial robustness, Data privacy, Systematic review.},
        month = {May},
        }