How to interpretate multiple histograms corresponding to each feature in multiple linear regression for relationship?
A histogram shows the distribution of values in a feature/variable. For instance in CHAS you see that there are two values (0,1), which means binary encoding (aka one-hot or dummy).

Histograms are helpful to understand the general structure of data and to detect things such as outlier or censoring. They are also good to get a quick idea of skewness in the data/distribution. Look at „age“ for example. You can see concentration of values at the top of the x-values. This likely is due to houses being older than 100 years are recorded as „100“ in the data. A similar pattern occurs for the price.

However, wrt „significance“ and correlation, histograms do not tell you a lot. For correlation, look at correlation measures. For significance (I guess you mean importance), you need to do sequential testing of features or you employ Lasso/Ridge regression for a more systematic approach.