Abstract: Flagellated bacteria exhibit significantly altered motility near solid–liquid interfaces, affecting key biological processes such as biofilm formation and pathogenic infection. In this study, we present a simplified in-line digital holographic microscopy (DHM) system tailored for high-throughput, label-free imaging of Escherichia coli (E. coli) swimming in near-surface environments. By applying a sliding median filter and mean normalization, we effectively suppress speckle noise and background artifacts in holograms. We introduce a morphology-aware workflow combining a voting algorithm, fast Fourier transform (FFT) analysis, and Bayesian optimization to robustly estimate bacterial positions and orientations, overcoming instability challenges common in non-spherical scattering models. Additionally, we employ a discrete dipole approximation (DDA) with a Levenberg–Marquardt (LM) optimizer to simulate and fit holographic interference patterns, achieving submicron axial precision and accurate tilt and azimuth angle measurements near surfaces. Through experiments tracking E. coli near surfaces, we quantitatively characterize swimming speed, trajectory geometry, and cell body orientation with high temporal resolution, revealing critical features of near-surface bacterial motility. Our results demonstrate the feasibility of single-beam DHM for rapid, quantitative tracking of bacterial surface behavior, providing an accessible and powerful tool for investigating microbe—interface interactions in biophysical and biomedical research.