This study addresses the critical need for accurate mapping of submerged terrain, which is essential for hydraulic modeling, environmental monitoring, and water resource management. Traditional bathymetric techniques, such as topographic surveys and acoustic soundings, face spatial continuity and usability challenges in shallow or vegetated waters. Recent advances, including Uncrewed Surface Vessels (USVs) equipped with GNSS and acoustic sensors, along with UAV-based photogrammetry for 3D modeling in clear waters, have expanded capabilities. However, optical methods suffer from depth underestimation due to light refraction, requiring geometric corrections. To address these limitations, the paper proposes a multi-sensor fusion workflow that integrates high-precision topographic data from total stations and GNSS, depth measurements from a USV equipped with a single-beam echo sounder, and UAV-derived optical bathymetry corrected for refraction using Structure from Motion (SfM) techniques. The goal is to combine each method's strengths to overcome their weaknesses and produce an accurate, high-resolution bathymetric model. Validation against ground truth data demonstrated significant improvements in data quality, aligning with standards for shallow-water mapping. Notably, the use of corrected UAV photogrammetry extended effective depth measurements to 4–5 meters, exceeding typical optical limits. The combined methodology ensures robust spatial coverage, precise georeferencing, and transparent independent measurements, making it particularly well-suited for complex lacustrine (lake) environments. The results highlight the operational benefits of using complementary technologies and suggest potential for further enhancement through Machine Learning and Deep Learning techniques to refine data integration and analysis.