The economy serves as the foundation of any civilization, and a primary determinant that shapes it is the level of joblessness. The overall prosperity of a population is likewise impacted by this parameter, underscoring its critical significance. Conventional techniques for forecasting such economic patterns offer limited understanding and fall short of the capabilities enabled by artificial intelligence (AI). Consequently, the study outlined in this paper adopts an AI-based methodology to address this crucial challenge, which is governed by numerous intricate interdependencies. The task of forecasting unemployment levels is approached as a time-dependent data modeling problem, where recurrent neural networks (RNNs) often yield commendable outcomes. The investigation described in this work applies echo state networks (ESNs), a specific category within RNN architectures. Nevertheless, this strategy requires careful optimization of hyperparameters, leading to the integration of metaheuristic strategies as a practical remedy. To fine-tune the ESN configurations, an adapted variant of the elk herd optimization algorithm (ELK) is employed, tailored to meet the distinct demands of the problem. The suggested approach exhibits improved accuracy compared to both contemporary advanced techniques and the standard ELK, based on widely accepted evaluation metrics for temporal prediction.