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The notion of a q-rung orthopair fuzzy soft rough set (<inline-formula> <tex-math notation="LaTeX">$^{q}ROFSRS$ </tex-math></inline-formula>) appeared as an extension of q-rung orthopair fuzzy set (<inline-formula> <tex-math notation="LaTeX">$^{q}ROFS$ </tex-math></inline-formula>) and q-rung orthopair fuzzy soft set (<inline-formula> <tex-math notation="LaTeX">$^{q}ROFSS$ </tex-math></inline-formula>) with the aid of rough set (RS) definition. Thus, <inline-formula> <tex-math notation="LaTeX">$^{q}ROFSRS$ </tex-math></inline-formula> and m-polar fuzzy set (<inline-formula> <tex-math notation="LaTeX">$_{m}PFS$ </tex-math></inline-formula>) are convenient to deal with uncertain knowledge which helps us to solve many problems in decision making. In this paper, we define the soft rough q-rung orthopair m-polar fuzzy sets (<inline-formula> <tex-math notation="LaTeX">$^{\text {q}}RO_{\text {m}}PFS$ </tex-math></inline-formula>) and q-rung orthopair m-polar fuzzy soft rough sets (<inline-formula> <tex-math notation="LaTeX">$^{q}RO_{\text {m}}PFSRS$ </tex-math></inline-formula>) through crisp soft and q- rung orthopair (q-RO) m-polar fuzzy soft approximation space. The related characteristics of these models are also studied. Then, we construct two new algorithms for these models to solve MADM issues. The successful application and corresponding comparative analyses proves that our proposed models are rational and effective.