In most cases Generalized Linear Models (GLMs) are popular tools for analyzing scientific phenomena, their limitations are becoming increasingly apparent. Theorical speaking I am proposing a new proposes a new approach that surpasses GLMs by incorporating the ability to learn from mistakes and update itself dynamically. This enhanced model would not only account for associations but also integrate the power of causal inference, leading to more precise and nuanced insights. As like large language models and AI tools. But In algebraic and real space functions analysis with Statistical estimation machine learning technique .