Artificial intelligence (AI) offers astonishing capabilities, with current achievement in using human experience at different phases of classical building. Only when AI actively collaborates with humans to co-create solutions as part of its intelligence will it reach its full potential. This study can use data convergence to combine AI simulation and human intelligence to increase decision-making as well as create a capability like to “teaming intelligence.“ The study of information extraction from tweets employs several techniques. It can comprehend human words and sentences due to Natural Language Processing (NLP), a subfield of AI. NLP blends statistical, deep learning (DL) as well as machine learning (ML) models with rule-based human language modelling. The goal of this study is to use NLP and pipelines for disaster tweet classification. Since tweets are by their very nature largely unstructured, text pre-processing—which entails eliminating unnecessary and extraneous words from the tweets—is a crucial step in the process. Text pre-processing, feature extraction, and modelling are all processes in the NLP pipeline. This use a TF-IDF vectorizer for pre-processing, which encompasses tokenization, stop word removal, tokenization, and feature extraction. To assess the data based on tweets, this work makes use of a hybrid classifier and compares with several ML and DL classification techniques. The proposed approach incorporates a hybrid classifier, comparing its performance against several ML and DL techniques. This study provides insights into optimizing NLP pipelines for real-world applications by evaluating the classifiers’ effectiveness on disaster-related tweet data.