<p>This study examines the field of grammatical inference, focusing on formal languages and the emerging domain of picture languages. It explores how grammatical inference methods can identify and construct grammars from given datasets, encompassing positive and, in some cases, negative samples. The survey highlights foundational contributions by researchers such as Gold and Angluin, discusses advancements in inference algorithms, and explores their applications across diverse areas, including pattern recognition, bioinformatics, and computational linguistics. Furthermore, it emphasizes the integration of grammatical inference with modern machine learning techniques, like reinforcement learning and neural networks, to tackle complex datasets and enhance the identification of target languages. The graphical analysis provides insights into the annual production trends of scientific articles. It highlights the most cited countries demonstrating significant academic influence. It identifies the key sources contributing to research. Finally, it applies Bradford’s Law to reveal the distribution of literature within the field based on the chosen keywords. Finally, we discuss open theoretical problems and outline future research directions that bridge classical grammatical inference and modern data-driven methods in diverse application settings.</p>