In Bayes theorem of a parameter $\theta$ with data $D$, we have:
$$P(\theta|D) = \frac{P(D|\theta)P(\theta)}{P(D)}$$
where I know $P(D)$ as the marginal likelihood. Is it true that the marginal likelihood is referred to as evidence in Bayesian statistics? If not what is commonly refered to as evidence?
$P(D)$ is the model evidence, unfortunately "model" is often dropped. The model evidence is also referred to as marginal likelihood.
Wikipedia calls the data $D$ the evidence.
The model evidence is defined as:
$\int\,P(D,\theta)d\theta = P(D)$
It is called the model evidence, since the larger its value, the more apt the model is generally fitting the data.
The term are loosely defined.
In your question, We use the term data ($D$) and parameter ($\theta$).
In other literature, people use another set of terms: evidence ($E$) and hypothesis ($H$), which are exactly the same thing to your $D$ and $\theta$.
Using the evidence and hypothesis combo, following notation is also widely used.
$$P(H|E) = \frac{P(E|H)P(H)}{P(E)}$$
Check the wikipedia page on Bayesian Inference, you can see the formula.
