Hydration level (HL) is a critical physiological indicator of human health and functional status, and accurate HL monitoring is essential for applications in healthcare, sports, and wellness assessment. However, existing methods are either invasive and inconvenient or noninvasive but limited by system complexity and insufficient accuracy. To address these limitations, this study proposes a methodological approach for noninvasive computerized HL estimation based on galvanic skin response (GSR) signals, termed the PSAML approach, which integrates principal component analysis (PCA), successive decomposition index (SDI), and machine learning (ML) classifiers. A representative GSR dataset was collected from three healthy subjects under dehydrated, normal, and overhydrated states in sitting, standing, and posture-independent scenarios. After preprocessing, including outlier removal, Butterworth filtering, and time-window segmentation, conventional time-domain features were extracted and compared with PCA- and SDI-based representations. Six ML algorithms were used for classification. The results show that the conventional feature method achieved a maximum accuracy of 63.97%, whereas PCA-based feature reduction significantly improved performance, with PCA+SVM, PCA+LR, and PCA+LDA achieving accuracies above 99% in most cases. SDI-based features also demonstrated strong performance with suitable classifiers under smaller time windows. These findings demonstrate that the proposed PSAML approach provides an accurate and efficient solution for wearable noninvasive HL monitoring.