<p>Vehicle dampers are essential for maintaining ride comfort and handling stability by regulating the forces transmitted through the suspension system. Conventional flow analysis techniques have been extensively utilised to assess damper performance, employing analytical models and empirical methods to replicate the fluid dynamics within the damper. This paper investigates the effectiveness of conventional flow analysis techniques used in damper assessment, such as Particle Image Velocimetry (PIV), dye injection, flow visualisation with transparent models, smoke/vapour visualisation, bubble visualisation, wind tunnel testing, shadowgraphy, and Schlieren photography, across diverse operational settings. While these traditional methods offer valuable insights into damper performance, their shortcomings in tackling real-world complexities underscore the need for integration with contemporary computational tools to enhance damper design and performance improvement. This research explores the potential for integrating conventional methodologies with contemporary computational technologies, such as computational fluid dynamics (CFD), to improve damper evaluation and provide more precise and optimised vehicle suspension designs. The study's observation concludes that conventional methods are helpful for baseline assessments, but their relevance in advanced damper design may be restricted. This study enhances the comprehension of traditional techniques in damper performance assessment and proposes avenues for future improvements, such as incorporating real-time data analysis and machine learning algorithms for enhanced predictive accuracy. This study distinguishes itself from prior research by providing a comprehensive analysis of the limitations of traditional approaches, while other studies predominantly concentrated on affirming the use of CFD in isolation.</p>