The GBM model is liked by practitioners for the modelling of stock prices for the following reasons:
(i) The solution is log-normal, so the stock price distribution varies between zero and infinity: which is what we would expect from a real-world stock price.
(ii) The model has independent increments, which means the future distribution of the stock only depends on its current price (i.e. $S_0$). Future distribution does not depend on previous returns or the path taken to reach the current stock price $S_0$. This is considered a good feature by those who believe in the efficient market hypothesis.
The shortcomings of the model are for example continuity of stock prices (stocks move in small jumps (ticks) and sometimes large jumps (opening gaps, auctions)).
The most important thing to note about the model though is that it is heavily dependent on the parameters (which is somewhat obvious): the volatility and the drift. The future distribution of your stock price will depend on which $\mu$ and $\sigma$ you choose. Specifically, the $\mu$ will determine the center of your future stock price distribution and $\sigma$ will drive the width of the future stock price distribution.
The GBM model is usually only used for pricing of stock forward or options, where you only care about the expectation of your future stock price distribution. In this case, you calibrate your model to liquid forward prices so that the drift term will make the expectation of the future distribution equal to the forward prices you observe in the market (and you can already guess from this that your drift most likely won't be constant, but will have to be time dependent, to match the various forwards of different maturities). You then use this calibrated model to price forwards and options that are not traded in the market.
I have never seen the GBM model used for predicting the future stock price of a particular stock. I have seen auto-recursive (AR) time series models trying to attempt that. But I would argue that the GBM model is only good for pricing of (simple) stock derivatives, and is not a good candidate for predicting future stock prices.
If you are interested in predicting future stock prices based on past observed prices, I would try looking into AR models: there are many variations and some are better than others, but I'd argue you'll be more likely to get more accurate results with AR rather than GBM.