The Fuzzy Markov Model is a fascinating domain for dealing with ambiguity in real-world scenarios. In type-2 fuzzy set (T2F), it has an uncertainty footprint, and the region circumscribed by the lower and upper interval membership functions is uncertain. In fuzzy sets, triangular norms (t-norms) are a valuabletool for understandingthe conjunctionin fuzzylogic and, as a result, determining where fuzzy setsintersect. Norms and conforms in triangular operations that generalise logical conjunction and disjunction. They also provide a natural explanation for the conjunction and disjunction in mathematical fuzzylogic semantics. Fuzzy Frankt-norms have been usedto verifythist-norm asthere aremany ofthe aggregation qualities oftrapezoidal interval type-2 numbers (TpIT2FNs) because triangular norm meets the compatibility with Frank norms. Frank t-norms provide more flexibility and robustness; this requires more justification in the information fusion process than other t-norms. Previous works on not concentrate on Frank’s norms. Other aggregation works on norms that are not flexible to get the solution. Because of that, the Frank norms are used for the hidden Markov model. We have also used them in the Viterbi method with TpIT2FNs for Fuzzy hidden Markov model in the staff selection process.