In an increasingly complex financial and operational landscape, accurate forecasting and robust risk assessment are critical for organizational resilience and decision-making. Time series-based quantitative risk models have emerged as pivotal tools in addressing these challenges by leveraging historical data to identify trends, patterns, and anomalies. These models enhance the precision of forecasting by integrating statistical techniques, machine learning algorithms, and advanced computational frameworks, enabling organizations to anticipate potential risks and develop informed strategies. This paper explores the evolution of time series-based models in risk management, highlighting their superiority over traditional approaches. Unlike static methods, these models dynamically adapt to changing conditions, providing real-time insights into volatile environments such as financial markets, supply chains, and operational systems. Advanced techniques like ARIMA, GARCH, and LSTM networks have further revolutionized risk modelling by improving the accuracy of predictions and mitigating the impact of uncertainties. A key focus is the application of these models in diverse industries, including finance, where they are used to predict asset prices and market volatility, and manufacturing, where they optimize supply chain operations and mitigate disruptions. Despite their advantages, implementing these models poses challenges related to data quality, model interpretability, and computational complexity, which are addressed through innovative solutions and strategies. By examining practical applications, success stories, and emerging trends, this paper underscores the transformative potential of time series-based quantitative risk models. It provides a comprehensive framework for leveraging these tools to enhance forecasting accuracy and risk assessment, ensuring organizations are better equipped to navigate uncertainty and achieve sustainable growth.