In software engineering, requirements prioritization is a critical stage that ensuresthe timely distribution of high-value structures while working with restricted resources. In this study, 19 popular requirements prioritization policies are associated, and their efficiency is studied from a range of approaches, counting scalability, accuracy, shareholder contribution, and related flexibility. The goal is to define which technique or techniques are best suitable for a particular project established on dynamics like team size, complexity of requirements, time, cost, and risk. This comparative study's main goal is to evaluate both modern techniques relating Artificial Intelligence, Fuzzy Logic, and Huge Language Models (LLMs) and more conservative methods such as Analytic Hierarchy Process (AHP), MoSCoW, Planning Game, and Value-Oriented Prioritization (VOP). The rewards of each method are examined concluded experiential case studies, which authenticate that LLM-based tools distribute automation and speed in Agile atmospheres, while AHP is dependable and dependable precisely. The procedure involved collecting and observing available research from 2015 to 2025 with an importance on relative presentation, shareholder satisfaction, and choice accuracy. Surveys, models, and relative testing were inspected to control the prizes and drawbacks of each process. The results show that hybrid mockups, such as restraint solvers, fuzzy AHP, and AI-assisted methods, perform better than outdated ones in relations of flexibility and usability, particularly in vibrant or important tasks. The revision proposes a context-aware method for operation, utilizing fusion/AI mockups where robotics and scalability are critical, MoSCoW for Agile groups, and AHP for systematic stability. In software development and condition manufacturing measures, this research supports in constructing well-read results.
bibi et al. (Thu,) studied this question.