Using uppercase $K$ for a random variable and lowercase $m,d,k_0$ for constants:
The number of balls in a particular bowl has a discrete uniform distribution with mean $\frac{m}{2}$ and standard deviation $\sqrt{\frac{m^2+2m}{12}}$.
Add up a large number $d$ of these i.i.d. then the the distribution of sum $K$ with mean $\frac{md}{2}$ and standard deviation $\sqrt{\frac{(m^2+2m)d}{12}}$ can be approximated using the central limit theorem, remembering $K$ is discrete with integer spacing.
So $$\Pr(K=k_0) \approx \Phi \left( \frac{k_0 +\frac12 -\frac{md}{2}}{\sqrt{\frac{(m^2+2m)d}{12}}} \right)- \Phi \left( \frac{k_0-\frac12 -\frac{md}{2} }{\sqrt{\frac{(m^2+2m)d}{12}}} \right) \approx \phi \left( \frac{k_0-\frac{md}{2}}{\sqrt{\frac{(m^2+2m)d}{12}}} \right) $$ where $\Phi$ is the cumulative distribution function and $\phi$ is the probability density function of a standard normal distribution.