The objective of this work is to apply artificial neural networks for solving inverse problems in the structural optimization of a fiber optic pressure sensor. For the sensor under investigation to achieve a desired accuracy, the change in the distance between the tips of the two fibers due to the applied pressure should not interfere with the phase change due to the change in the density of the air between the two fibers. Therefore, accurate dynamic analysis and structural optimization of the sensor is essential to ensure the accuracy of the measurements provided by the sensor. To this end, a normal mode analysis and a transient response analysis of the sensor were performed by combining commercial finite element analysis package, MSC/NASTRAN, and MATLAB. Furthermore, a parametric study on the design of the sensor was performed to minimize the size of the sensor while fulfilling a number of constraints. In performing the parametric study, the need for a relationship between the design parameters and the response of the sensor was fulfilled by using a neural network. The whole process of the dynamic analysis using commercial finite element analysis package and the parameter optimization of the sensor were automated within the MATLAB environment.