Bangla is one of the world’s most widely spoken languages, yet it remains significantly under-resourced in natural language processing (NLP). Existing efforts have focused on isolated tasks such as Part-of-Speech (POS) tagging and Named Entity Recognition (NER), but comprehensive, integrated systems for core NLP tasks including Shallow Parsing and Dependency Parsing are largely absent. To address this gap, we present BanSuite, a unified Bangla NLP ecosystem developed under the EBLICT project. BanSuite combines a large-scale, manually annotated Bangla Treebank with high-quality pretrained models for POS tagging, NER, shallow parsing, and dependency parsing, achieving strong in-domain baseline performance (POS: 90.16 F1, NER: 90.11 F1, SP: 86.92 F1, DP: 90.27 UAS). The system is accessible through a Python toolkit (Bkit) and a Web Application, providing both researchers and non-technical users with robust NLP functionalities, including tokenization, normalization, lemmatization, and syntactic parsing. In benchmarking against existing Bangla NLP tools and multilingual Large Language Models (LLMs), BanSuite demonstrates superior task performance while maintaining high efficiency in resource usage. By offering the first comprehensive, open, and integrated NLP platform for Bangla, BanSuite lays a scalable foundation for research, application development, and further advancement of low-resource language technologies. A demonstration video is provided to illustrate the system’s functionality in https://youtu.be/3pcfiUQfCoA