Accurate magnetic signature prediction is the most challenging task in the path of active magnetic signature minimization. This paper discusses the effect of the change in hull thickness, relative permeability, and radius of the object, under consideration for the detailed understanding of the magnetic signatures. The traditional magnetic experimental based method using a 12 coil structure is implemented for a double hull vessel. However, the time required and effort involved in predicting the magnetic signature is much higher. Hence a multiple linear regression-based statistical machine learning (ML) technique for the magnetic signature prediction is proposed. The nonlinear magnetic behavior of the naval vessel is assumed to be linear because the hysteresis curve in the range of the earth’s ambient field is linear. Hence a linear relationship is developed between the magnetic signatures and each of the components of the ambient earth’s magnetic field. The machine learning-based model also considers the cross-talk between the longitudinal, athwartship, and vertical signatures. The predicted magnetic signatures using the ML technique and the experimental based techniques are compared. The ML method is found to be accurate, fast, and precise as compared to the laboratory-based method, which has strict and continuous requirements for hardware magnetic facility. This method is very simple to implement and directly contributes to the development of real-time degaussing current calculation algorithm.