This review article presents a comprehensive conceptual model for requirements elicitation in software development, integrating advancements and innovations to address traditional challenges. Requirements elicitation is a critical phase in the software development lifecycle, ensuring that stakeholders' needs are accurately captured and translated into system specifications. Traditional methods often face challenges such as ambiguity, inconsistency, and evolving requirements, leading to project delays and cost overruns. The proposed model leverages techniques like natural language processing (NLP), machine learning (ML), and human-computer interaction (HCI) to enhance the accuracy and efficiency of requirements elicitation. NLP enables automated analysis of textual requirements, extracting key information and identifying implicit requirements. ML algorithms predict potential changes in requirements based on historical data, enabling proactive management of evolving requirements. HCI principles enhance stakeholder engagement and collaboration through interactive interfaces and visualization tools. The model also emphasizes context-awareness, considering organizational, cultural, and environmental factors to adapt elicitation strategies. It addresses conflicting requirements through systematic prioritization and negotiation, using multi-criteria decision-making techniques. The model advocates for iterative refinement and validation of requirements throughout the development lifecycle, ensuring adaptability and resilience. By integrating these elements, the model aims to improve the accuracy, efficiency, and stakeholder satisfaction in requirements elicitation, ultimately contributing to the successful delivery of high-quality software systems.This review article presents a comprehensive conceptual model for requirements elicitation in software development, integrating advancements and innovations to address traditional challenges. Requirements elicitation is a critical phase in the software development lifecycle, ensuring that stakeholders' needs are accurately captured and translated into system specifications. Traditional methods often face challenges such as ambiguity, inconsistency, and evolving requirements, leading to project delays and cost overruns. The proposed model leverages techniques like natural language processing (NLP), machine learning (ML), and human-computer interaction (HCI) to enhance the accuracy and efficiency of requirements elicitation. NLP enables automated analysis of textual requirements, extracting key information and identifying implicit requirements. ML algorithms predict potential changes in requirements based on historical data, enabling proactive management of evolving requirements. HCI principles enhance stakeholder engagement and collaboration through interactive interfaces and visualization tools. The model also emphasizes context-awareness, considering organizational, cultural, and environmental factors to adapt elicitation strategies. It addresses conflicting requirements through systematic prioritization and negotiation, using multi-criteria decision-making techniques. The model advocates for iterative refinement and validation of requirements throughout the development lifecycle, ensuring adaptability and resilience. By integrating these elements, the model aims to improve the accuracy, efficiency, and stakeholder satisfaction in requirements elicitation, ultimately contributing to the successful delivery of high-quality software systems.