<![CDATA[Apparel Micro, Small, and Medium Enterprises (MSMEs) frequently provide significant sales data that is inadequately leveraged for strategic business advancement. This study intends to examine sales data utilising the K-Means Clustering technique within the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The dataset consists of 360 sales records during the period 2021–2023, including factors such as amount sold, unit price, and total turnover. The analysis adheres to the systematic CRISP-DM phases: business understanding, data understanding, data preparation, modelling, assessment, and deployment. The assessment outcomes, quantified by the Silhouette Score, determined two ideal clusters for the dataset. Cluster 1 denotes well-performing products distinguished by elevated sales volume, comparatively high unit prices, and substantial turnover. Conversely, Cluster 2 comprises underperforming products characterised by diminished sales volume, reduced unit pricing, and negligible turnover. These findings offer a data-driven basis for MSMEs to develop more efficient marketing strategies and inventory management procedures. By categorising products according to performance, business owners can prioritise high-value commodities and optimise inventory for less productive categories. This study indicates that employing K-Means clustering inside the CRISP-DM framework effectively converts raw sales data into meaningful business intelligence for the garment sector. Subsequent study may enhance this technique by integrating external variables, including seasonal trends or customer demographics, to improve clustering precision. REFERENCES Abdul-Azeez, O., Ihechere, A. O., & Idemudia, C. (2024). Enhancing business performance: The role of data-driven analytics in strategic decision-making. 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