The thesis titled ``Prescriptive Analytics: Review of Frameworks and Critical Evaluation of PrescrX'' explores the landscape of decision-making systems powered by data-driven prescriptive techniques. After reviewing existing frameworks, the work introduces PrescrX---a custom system developed to tackle practical optimization problems based on predictive input. The author outlines how prescriptive analytics bridges the gap between analytical insight and actionable strategy. The literature review delves into well-established models including optimization, simulation, and decision analysis, with particular attention to the integration of machine learning and mathematical programming. The PrescrX tool is then analyzed in depth, both in terms of its operational logic and its architectural design.