Identifying subsurface mineralized zones is of immense importance in the mineral exploration industry. It is challenging for exploration geophysicists to delineate the structures associated with it. One such structure, a dyke is of immense importance for locating mineral or ore bodies. Hence, in the present study, inverse modeling of the magnetic anomalies produced by a 2D dyke like structure was carried out using a very fast simulated annealing (VFSA) global optimization algorithm. The parameters such as amplitude coefficient, depth, half-width, origin, and dip angle of 2D inclined dyke like bodies were interpreted. The inversion approach was then applied to the synthetic noise-free, noisy data, and three field examples for a single body from known magnetic anomalies from Pima copper mine in the US state of Arizona, Marcona iron mine in Peru, Bayburt-Sarihan skarn mineralization from Turkey, and one multiple dyke anomalies from Beldih uranium mineralization, India were taken for such study. It has been found that VFSA optimization can efficiently interpret all the parameters with minimum uncertainty. However, parameters such as amplitude coefficient, depth, and half-width shows some uncertainty in estimating the model parameters describing an equivalent solution, but within the minimum misfits and are close to the accurate models with the slightest uncertainty. Other investigation from 2D cross plots and 3D volume space analysis also reveals the same. The present findings show that VFSA can provide a good result in agreement with the actual data and formerly obtained results from the different interpretation and inversion methods and a priori information for field data. Hence, it can be determined that the present VFSA algorithm can be very effective for understanding the magnetic anomalies derived from 2D dykes with or without a priori information.