Forecasting and Resource Management in Long-Term IT Projects

This research addresses the challenges of resource forecasting and management in long-term IT projects by integrating machine learning, blockchain verification, and full-stack automation. The study compares conventional forecasting approaches with AI-enhanced and convergent models in terms of prediction accuracy and adaptability. Results show that hybrid models with blockchain confirmation outperform others in accuracy and scalability. Graphs and system diagrams illustrate key performance differentials. The proposed model provides a secure and predictive infrastructure tailored to dynamic software environments. The work also highlights the value of secure front-end architecture and adaptive interfaces to mitigate resource-related risks in large-scale development.