K Nearest Neighbor using K-D Tree (What ratio makes K-D beneficial)?
There's no way to tell for sure without trying out.  That said, a crude rule of thumb is that k-d trees tend to be useful when the number of dimensions is up to about 10-20, but in higher dimensions, often they are little better than a naive linear search over all points.
See also Is there some theoretical verification or explanation of why KDTree gets slower when the dimensionality gets higher?, How can I make k nearest neighbor queries fast on unit hypersphere?, https://en.wikipedia.org/wiki/K-d_tree#Degradation_in_performance_with_high-dimensional_data