For the sake of simplicity, let's lets assume that $\Delta x$ is constant. Then
$$\sum_{i=1}^n f(x_i^*)\Delta x = \Delta x \sum_{i=1}^n y_i$$
where $y_i = f(a + i\Delta x)$ for $i=0\dots n$.
Sampling $(x, f(x))$ at the $n+1$ equally-spaced points
$\left\{(x_i, y_i) \right\}_{i=0}^n$, you can use
$\displaystyle \Delta x \sum_{i=1}^n y_i$
as an approximation of $\displaystyle \int_a^b f(x) dx$.
Numerically, if you want a better approximation, then you make $n$ larger. Realistically, numerical problems occur when $n$ gets larger so your accuracy is limited; but usually good enough to be useful.
Mathematically, $\Delta x$ depends on the value of $n$.
As $n$ get larger, $(n \to \infty)$,
$\Delta x$ gets smaller, $(\Delta x \to 0)$,
and $\displaystyle \sum_{i=1}^n y_i$ gets larger,
$\left(\displaystyle \sum_{i=1}^n y_i \to \infty \right)$.
But, in most cases, $\displaystyle \lim_{n\to \infty}\Delta x \sum_{i=1}^n y_i$ approaches a limit and that limit is defined as $\displaystyle \int_a^b f(x) dx$
In the most general case, the sum is $\sum_{i=1}^n f(x_i^*)\Delta x_i$ where $\Delta x_i = x_{i+1}-x_i$. But the argument is still, more or less, the same.