This study establishes a robust foundation for leveraging artificial intelligence in predictive project estimation. The proposed GA-ANN hybrid framework demonstrated significant advancements in accuracy, computational efficiency, and adaptability, marking a transition from static, rule-based estimation methods to intelligent, data-driven systems. The findings underscore AI’s transformative role in enhancing project planning precision and open new frontiers for research and industrial innovation in predictive analytics and software engineering. The study opens several promising avenues for further exploration: Multi-Objective Optimization: Extending the model to optimize multiple objectives (e.g., cost, time, quality) simultaneously. Explainable AI (XAI): Integrating model interpretability techniques such as SHAP or LIME to enhance transparency. Transfer Learning: Applying the trained GA-ANN model to different domains and adapting it through fine-tuning for cross-domain prediction. Real-Time Deployment: Embedding the model into cloud-based project management platforms to provide live estimations. Automated Data Pipelines: Incorporating AutoML frameworks for feature engineering, model selection, and tuning.