Bathymetric mapping is vital for navigation, coastal management, and marine habitat assessment. Traditional methods use
satellite reflectance data and machine learning (ML), supported by echo-sounding field data. This study explores the underuti
lized potential of chlorophyll concentration for water depth inference, introducing it as a novel alternative for bathymetry and
highlighting advanced ML techniques for improved mapping precision. The novel approach, applied at two sites in Egypt,
Jemsha region (Gulf of Suez coast) and New Heaven Resort (south of Marsa Alam on the Red Sea coast), uses two main
strategies. First, water depth was estimated from MODIS satellite chlorophyll data and validated with echo-sounding field
data, yielding an RMSE of 1.5 m, R2 of 0.55, and precision of 0.836 for Jemsha, and an RMSE of 2.5 m, R2 of 0.1, and
precision of 0.979 for New Heaven. Second, water depth was derived from Sentinel-2 satellite reflectance data using a new
ensemble ML (EM) technique, refined from three well-known bathymetry models, and validated similarly. Results showed
an RMSEof1.3 m, R2 of 0.5, and precision of 0.836 for Jemsha, and an RMSE of 2 m, R2 of 0.3, and precision of 0.979 for
New Heaven. These findings are globally significant, addressing bathymetric data scarcity in areas with limited field data or
logistical constraints, while advancing methods for sustainable coastal management and marine conservation.