Predicting non-stationary time series is still a tough nut to crack: the statistical properties may vary, and so will the temporal dynamics. In this context, a robust and interpretable approach involving the integration of a Takagi–Sugeno neuro-fuzzy inference system and evolutionary multi-objective optimization is proposed. The NSGA-II optimization procedure minimizes prediction accuracy alongside model complexity, evolving the fuzzy rule base and membership parameters and the linear consequents. Contrary to the conventional opaque models, a neuro-fuzzy architecture affords some degree of transparency, letting one see how the functional temporal patterns have been learned. Results of the experiments conducted demonstrate the ability of the system to adapt to structural changes in the data, with a corresponding better capability of reducing residual errors and increasing model compactness using a synthetic non-stationary dataset. Further, residual oscillations and fuzzy rule surfaces, alongside the convergence behavior and the Pareto optimality, present powerful evidence in support of the proposed model’s effectiveness and interpretability. The proposed framework represents a potent alternative for real-time adaptive forecasting in dynamic environments where precision and explainability are both crucial. Simulated non-stationary time series will be good enough to justify the approach of lag-embedding. Thoughtful integration of interpretable fuzzy logic and adaptive learning via evolutionary optimization would strengthen the contribution.