Obtaining soil parameters through laboratory tests and solving the governing equations that describe soil settlement can be time-consuming, making immediate on-site predictions of soil settlement challenging. In-situ testing provides a more efficient approach to obtain soil parameters than laboratory tests. Data from the Piezocone penetration test (CPTu) can be used for on-the-spot interpretation of soil mechanical parameters, which can then be incorporated into the governing equations for soil settlement calculation. Physics Informed Neural Networks (PINNs) algorithm uses automatic differentiation method to directly embed partial differential equations (PDEs) into a deep learning neural network and provides solution for these PDEs in a cost-effective manner compared to traditional numerical methods. In this paper, a framework integrating data from CPTu and PINNs to predict soil settlement is proposed and evaluated through comparison with numerical simulations from Finite Element Methods (FEMs). Results show that the framework gave a reasonably good agreement with the FEMs benchmark while substantially reduced the computation time. This method allows for immediate on-site prediction of soil settlement during site investigations, thus better guiding surveying and construction activities.