A Comprehensive Study of Bengali Language Tools and Applications for Artificial Intelligence Research

ABSTRACT Bengali (Bangla) is not only one of the most widely spoken languages in the world, but also a low‐resource language as far as artificial intelligence (AI) is concerned. Despite recent advances, the creation of complete and unified Bengali AI remains a challenging issue because of a lack of annotated data, disjointed tools, and a lack of benchmarking frameworks. The paper provides an in‐depth analysis of the current Bengali language tools, datasets, models, and applications in the key aspects of AI, including natural language processing, machine translation, speech technologies, optical character recognition, and multilingual modeling. Based on a comprehensive literature review and comparison, the study presents the major steps made by classical, machine learning, and deep learning methods and points out the most important research gaps. These developments are essential to enable practical applications like online communication, education, voice assistants, and content moderation, and to provide scalable, accessible AI to millions of people who speak Bengali. The review indicates that the majority of the available literature addresses individual activities and does not cover a combination of text, speech, and multimodal pipelines. Moreover, the problems of the dialectal difference, inconsistency of spelling, the use of code‐mixed language, and insufficient real‐life testing are still undeveloped. To overcome such gaps, the paper suggests a systematic taxonomy of the Bengali AI resources and provides the direction of future research with a focus on unified benchmarks, resource growth, multilingual learning, and the comprehensive development of datasets. The current study will facilitate the establishment of a more powerful, scaled, and application‐oriented Bengali AI ecosystem.