Let $T_1, T_2, \cdots$ be i.i.d. exponential r.v.s with rate 1. Then the sum $S_n = T_1 + \cdots + T_n$ has the gamma distribution of rate 1 and order $n$:
$$ \Bbb{P}(S_n \leq x) = \int_{0}^{x} \frac{t^{n-1}e^{-t}}{(n-1)!} \, dt = \frac{\gamma(n, x)}{\Gamma(n)}, \quad x \geq 0. $$
Now by the classical CLT, if $Z \sim \mathcal{N}(0, 1)$ denotes any standard normal variable, it follows that
$$ \frac{\gamma(n, n)}{\Gamma(n)}
= \Bbb{P}( S_n \leq n )
= \Bbb{P}\left( \frac{S_n - \Bbb{E}S_n}{\sqrt{\mathrm{Var}(S_n)}} \leq 0 \right)
\xrightarrow[n\to\infty]{} \Bbb{P}(Z \leq 0) = \frac{1}{2}. $$
Alternatively, let $(N_t)$ be a Poisson process of rate 1. Then the distribution of $S_n$ is the same as the distribution of the $n$-th arrival time of $(N_t)$. Thus
$$ \Bbb{P}(S_n \leq n) = \Bbb{P}(N_n \geq n) = \sum_{k=n}^{\infty} \frac{n^k}{k!}e^{-n}. $$
We can prove that it converges to $1/2$ as well. For a purely analytic solution, see this answer, for instance.