The research examines e-commerce and e-business performance increase through model-based optimization techniques and systematic assessment methods. This study solves digital commerce issues through an integrated solution which applies superior protection techniques alongside user interface optimization and operational refinement methods. Empirical analysis of real-world data reveals major performance improvements along multiple key metrics that demonstrate user retention growth to 27% and average monthly revenue expansion at 32% as well as system response time reduction to 45%. The newly implemented model increased average order value by 23% and cut down security events by 64%. Quantitative and qualitative methods within the research tangled to explore the success of the proposed framework that implements blockchain technology along with artificial intelligence and machine learning components to enhance system performance. The research presents relevant observational data about actual e-commerce optimization methods together with theoretical observations about digital business betterment. Results from this research prove that the proposed methodology enables the development of stronger and more efficient e-commerce solutions which focus on user needs to help businesses succeed in digital competition.