Multitask learning (MTL) is a machine learning paradigm in which several related tasks are learned simultaneously to improve generalization performance. Kernel-based methods provide a mathematically rigorous and flexible framework for MTL, especially when training data are limited or uncertainty estimation is important. This study examined the research landscape of MTL using the Scopus database. The findings reveal that MTL has experienced remarkable growth in recent years, with over 93% of publications produced between 2016 and 2026, demonstrating its increasing relevance in modern scientific and technological research. The subject-area restriction further confirmed the highly interdisciplinary nature of the field, particularly across computer vision, medical imaging, predictive analytics, and artificial intelligence applications. Despite the rapid expansion of deep learning-based multitask approaches, the analysis identified only a very limited number of studies specifically focused on kernel-based MTL, indicating a significant research gap within the literature. This scarcity suggests that kernel-based methods remain largely underexplored despite their strong mathematical foundations, interpretability, and effectiveness in nonlinear modeling. The study therefore concludes that kernel-based MTL presents substantial opportunities for future theoretical development and practical applications, making it a promising direction for advancing MTL research.