The detection of focus and unfocus is critical in education, healthcare, and human–computer interaction. Electroencephalography (EEG) offers a non-invasive and real-time approach to assessing brain activity related to attention, yet challenges persist due to individual variability, non-stationary signals, noise, and limited labeled datasets. This paper reviews current trends in EEG-based focus detection, with an emphasis on deep learning (e.g., CNN, LSTM), meta-learning (MAML), and self-supervised learning (SSL). Publication analysis shows a significant rise in interest, with deep learning studies increasing from 10 in 2015 to more than 3,000 in 2025, while SSL and meta-learning have rapidly emerged since 2020. Our contributions are threefold: (1) identification of major challenges in EEG-based attention detection, including data scarcity and adaptability across subjects; (2) comparative evaluation of learning strategies in terms of data requirements, adaptability, and computational complexity; and (3) discussion of implementation pathways and application areas spanning brain–computer interfaces, neurofeedback, education, mental health, and autonomous systems. This review highlights promising methodologies for improving accuracy, generalizability, and efficiency, underscoring the potential of adaptive AI-driven EEG systems to advance both research and real-world applications.