Reverse design of highly GeO₂-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network (NN) combined with a particle swarm optimization (PSO) algorithm. Firstly, the NN model designed to predict optical fiber dispersion is trained with an appropriate choice of hyperparameters, achieving a root mean square error (RMSE) of 9.47×10^-7 on the test dataset, with a determination coefficient (R²) of 0.999. Secondly, the NN is combined with the PSO algorithm for the inverse design of dispersion-flattened optical fibers. To expand the search space and avoid particles becoming trapped in local optimal solutions, the PSO algorithm incorporates adaptive inertia weight updating and a simulated annealing algorithm. Finally, by using a suitable fitness function, the designed fibers exhibit flat group velocity dispersion (GVD) profiles at 1 400—2 400 nm, where the GVD fluctuations and minimum absolute GVD values are below 18 ps∙nm^-1∙km^-1 and 7 ps∙nm^-1∙km^-1, respectively.