The textile industry is one of the most significant industries in Sri Lanka, which gives a massive economic income to the country. Dyeing is an important process in the apparel industry since it directly impacts the exports if it does not have the accurate color consistency that’s needed. Such inaccuracies in color consistency lead to poor product quality, which affects the reputation of the brand and customer satisfaction. The most common method of measuring color concentration is using the UV- Vis spectrophotometer. It’s accurate but high in cost and needs trained professionals for operation. This study shows the advantages and disadvantages of existing traditional methods and how newly developed methods show enhancement in process efficiency. Smartphone colorimetry has been introduced as a new approach, but it still lacks some features to be used in a fully automated industrial environment. Vision-based machine learning systems are known to predict dye baths under controlled lighting conditions in automated industrial environments, where image processing techniques will extract relevant color features (RGB values). These values will be analyzed using machine learning algorithms trained by using laboratory reference data. This will allow for timely adjustments and process optimization. This paper addresses how the textile industry can enhance its process efficiency by knowing the strengths and limitations of different techniques of monitoring dye concentration. It also demonstrates how future expansions can be made by developing a low-cost, fully automated system using machine learning integrated with vision-based systems which can enhance the system’s accuracy, and it’s affordable and automated. The right color consistency will help in better dye usage and less material waste. Expected benefits are improved process control and cost-effective automation aligned with AI-driven solutions. This paper shows economically affordable and sustainable textile production practices.