This work focuses at how to construct strong mistake detection systems by combining sophisticated AI techniques with extremal combinatory concepts. The study uses mathematical frameworks based on vector measure spaces and orthogonal sequences to develop new combinatorial models that can discover complicated patterns of mistakes. The proposed method uses AI-driven algorithms that work properly with combinatorial systems to make popularity greater accurate and proof against noise. Its miles centered on the way to efficiently describe data and use characteristic engineering to file complex combinatorial mistakes patterns. This proves that it can be used for actual-world conversation and data shop systems.