Wind energy is considered to be of great importance for promoting energy transition and achieving net-zero carbon emission. Reliable modeling and monitoring of the near-subsurface geology are crucial for successful wind farm selection, construction, operation, and maintenance. For optimal characterization of shallow seafloor sediments, 2D ultrahigh-resolution (UHR) seismic survey and 1D cone-penetration testing (CPT) often are acquired, processed, interpreted, and integrated for building 3D ground models of essential geotechnical parameters such as friction. Such a task faces multiple challenges, such as limited CPT availability, strong noise contamination in UHR seismic data, and heavy manual efforts for completing the traditional workflows, particularly acoustic impedance inversion. This study accelerates the integration by a semisupervised learning workflow with three highlights. First, it enables geotechnical parameter estimation directly from UHR seismic data without impedance inversion. The second comes from the use of a pretrained feature engine to reduce the risk of overfitting while mapping massive UHR seismic data with sparse CPT measurements through deep learning. More importantly, it allows incorporating other geologic/geophysical information, such as a predefined structural model, to further constrain the machine learning and boost its generalization capability. Its values are validated through applications to the Dutch wind farm zone for estimating four geotechnical parameters, including cone-tip resistance, sleeve friction, pore-water pressure, and the derived friction ratio, in two example scenarios: (1) UHR seismic data only and (2) UHR seismic data and an 11-layer structural model. The results verify the feasibility of data-driven geotechnical parameter estimation. In addition to the two demonstrated scenarios, our workflow can be further customized for embedding more constraints, e.g., prestack seismic and elastic/static property models, given their availability in a wind farm of interest.