Arsenic contamination in water resources poses a serious environmental and public health challenge because of its toxicity, persistence, and widespread occurrence in groundwater systems. Among various treatment technologies, adsorption using modified natural materials has attracted increasing attention due to its low cost, operational simplicity, and high removal efficiency. In this study, the arsenic removal performance of Potassium Permanganate-Treated Laterite (PPTL) was investigated under different operational conditions, and Principal Component Analysis (PCA) was applied to interpret the multivariate relationships governing the adsorption process. Synthetic arsenic-contaminated wastewater with an initial concentration of 100 ppb was treated using PPTL under varying pH values, adsorbent masses, and interaction times. Descriptive statistics, correlation analysis, and PCA were performed using standardized datasets. The experimental results showed that arsenic removal efficiency ranged from 63.99% to 92.28%, with the highest removal achieved at pH 9, adsorbent mass of 1 g, and interaction time of 45 min. Correlation analysis revealed that adsorbent mass exhibited the strongest positive correlation with removal efficiency (r = 0.948). PCA results demonstrated that the first two principal components explained 88.14% of the total dataset variance, indicating that the PCA model effectively represented the adsorption system. PC1 was strongly associated with adsorbent mass and removal efficiency, whereas PC2 mainly reflected the influence of pH and interaction time. The findings confirm that adsorbent availability was the dominant factor controlling arsenic adsorption dynamics. Furthermore, the study demonstrates that PPTL is a promising low-cost adsorbent for arsenic-contaminated water treatment and highlights the effectiveness of PCA for interpreting complex environmental treatment datasets.