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As far as I know it's very unlikely that a categorical variable with billions of possible values could be a good predictor for a ML model, but there is certainly some underlying information related to the IP address which are good predictors. So it's a problem of feature engineering not in an technical sense but in a design sense, i.e. using expert knowledge in order to provide the most relevant information to the model.
I don't know the task or the data but you could study what makes the IP useful:
location: maybe using a feature representing the country or a more specific location based on the IP would work.
historically known IP: for example some boolean features could represent whether this IP connected in the past hour/day/week/...
third-part info: features representing whether this IP belongs to some whitelist/blacklist