How to interpretate multiple histograms corresponding to each feature in multiple linear regression for relationship? Used matplotlib to plot the histograms for each feature in  Boston dataset available in scikitlearn library.
How to interpretate the histograms to determine the correlation or significance of that feature in model training?
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.