Multivariate locally stationary functional time series provide a flexible framework for modeling complex data structures exhibiting both temporal and spatial dependencies while allowing for time-varying data generating mechanism. In this paper, we introduce a specialized portmanteau-type test tailored for assessing white noise assumptions for multivariate locally stationary functional time series without dimension reduction. A simple bootstrap procedure is proposed to implement the test because the limiting distribution can be non-standard or even does not exist. Our approach is based on a new Gaussian approximation result for a maximum of degenerate $U$-statistics of second-order functional time series, which is of independent interest. Through theoretical analysis and simulation studies, we demonstrate the efficacy and adaptability of the proposed method in detecting departures from white noise assumptions in multivariate locally stationary functional time series.