Transcranial Magnetic Stimulation (TMS) is a non-invasive neuromodulation technique with applications in brain mapping and effective neuronal connectivity. In its repetitive mode, it is used for the treatment of neurological and psychiatric disorders. It functions on the principle of electromagnetic induction, where generated magnetic fields (H-field) induce electric field (E-field) that stimulates the brain’s neurons. With TMS studies, accurate estimation of the induced E-field is usually necessary. However, this requires a lot of processes, including the three-dimensional head model generation from magnetic resonance images (MRI) using the SimNIBS software and finite element analysis to calculate the induced E-field. These processes are time-consuming and computationally expensive. Additionally, with each head model’s uniqueness, outcomes cannot be generalized across a particular population as the intensity of E-field is subject-specific. In this research, the authors develop deep convolutional neural network (deep CNN) models to determine the intensity of the induced E-field directly from the patient’s MRI scan and across different coil types. We trained CNN models from anatomically realistic head models and across 16 coil types to predict the induced E-field in the brain and scalp (E-Max brain and scalp), and the volume of stimulation of the brain and scalp (V-half brain and scalp) from T1-weighted MRI scans. Using a deep CNN model, the processing time for estimating the induced E-field is significantly reduced, which is helpful both to clinicians and researchers as the need to create subject-specific anatomical head structures is eliminated. Also, there will be no need for additional stimulation sessions with the different coil types for TMS patients as the deep CNN model can predict the outcome from each coil type. The other advantage of the deep CNN model is that the E-field from the different coil types can be compared simultaneously.