<p>Within software development life-cycle, requirements guide the entire development process from inception to completion by ensuring alignment between stakeholder expectations and the final product. Requirements extraction from miscellaneous information is a challenging and complex task. Manual extraction of requirements is not only prone to human error but also contributes to increased project costs and delayed project timelines. To automate the requirement extraction process, researchers have investigated the potential of deep learning architectures, large language models (LLM) and generative language models such as ChatGPT and Gemini. However, the performance of requirements extraction could be further enhanced through the development of predictive pipelines by utilizing the combined potential of language models and deep learning architectures. To develop a powerful AI application for requirements extraction by utilizing the combined potential of LLMs and DL architectures, this study presents ReqNet framework. The framework encompasses 7 most widely used LLMs variants (small, large, Xlarge, XXlarge) and 2 DL architectures (LSTM, GRU). The framework facilitates the development of three distinct types predictive pipelines, namely standalone LLMs, LLMs + external classifiers and an ensemble of multiple LLMs representation + external classifiers. Extensive experimentation of 48 predictive pipelines across 2 public core datasets and 1 independent test set, demonstrates that predictive pipelines made up from LLMs and DL architectures generally exhibited superior performance compared to pipelines solely reliant on LLMs. In addition, a ensemble of three distinct LLMs (ALBERT, BERT and XLNet) and LSTM classifier achieved a 3% improvement in F1-score over state-of-the-art predictors on the PURE dataset, a 10% improvement on the Dronology dataset and a 3% improvement on the RFI independent test set.</p>