Background: Accurate Software Effort Estimation (SEE) remains one of the most critical and persistent challenges in software engineering. Traditional algorithmic models lack adaptability, while standard machine learning (ML) approaches require extensive feature engineering and often fail to capture complex, non-linear project dynamics. Deep Learning (DL) offers a promising alternative but is highly sensitive to hyperparameter configuration and architectural choices, making optimization a significant barrier.

Objective: This research proposes and validates a novel, synergistic hybrid framework, "Deep-MetaSEE," designed to overcome these limitations. The framework integrates a hybrid deep learning model (Conv-LSTM) with an Enhanced Grey Wolf Optimizer (EGWO).

Methods: The Conv-LSTM core is designed to automatically extract hierarchical spatial and temporal features from project data. The EGWO, an improved metaheuristic algorithm incorporating dynamic weighting and Lévy flight mechanisms, performs a multi-objective optimization to simultaneously identify the optimal feature subset, Conv-LSTM architecture (layers, nodes), and training hyperparameters (e.g., learning rate, dropout). The Deep-MetaSEE framework was rigorously evaluated on three public benchmark datasets (Desharnais, COCOMO81, Maxwell) and compared against traditional ML (Random Forest, SVR), standalone DL (ANN, CNN, LSTM), and other hybrid models (PSO-ANN, GWO-ANN) using standard metrics (MMRE, PRED(25), MAE).

Results: Empirical results demonstrate that the Deep-MetaSEE framework achieves statistically significant (p < 0.05) and superior estimation accuracy, consistently outperforming all baseline models across all datasets. The EGWO component also demonstrated faster convergence to a better optimal solution compared to standard GWO.

Conclusion: The proposed synergistic hybrid framework provides a robust and highly accurate solution for SEE. By automating feature selection and optimizing deep learning architecture simultaneously, Deep-MetaSEE addresses key gaps in current estimation literature and offers a powerful data-driven tool for project managers.