Effective inventory management is crucial for businesses, particularly when dealing with goods that deteriorate over time. This abstract presents a review of mathematical models aimed at optimizing inventory management for deteriorating goods. Traditional inventory models often fail to account for deterioration, leading to suboptimal inventory decisions and increased costs. To address this challenge, various mathematical approaches have been developed. Deterministic modeling techniques utilize equations to describe the relationship between inventory levels, demand, and deterioration rates. Stochastic modeling incorporates uncertainty in demand and deterioration rates using techniques such as stochastic differential equations and Markov processes. Optimization methodologies, including dynamic programming and heuristic algorithms, are employed to determine optimal inventory policies under deteriorating conditions. Recent advancements in machine learning enable predictive analytics, enhancing forecasting accuracy and decision-making in inventory management. These mathematical models and techniques offer businesses the opportunity to minimize holding costs while ensuring sufficient inventory levels to meet demand and mitigate stockouts.