All of the listed "facts" or "features" can be incorporated into an index (e.g. with Lucene) or fed into ML-models (logistic regression, trees, neural nets, etc).

Without going into specifics, I've seen ranking treated as a multi-stage algorithmic problem. Initially, you rank results with a lower-quality but low-latency ranker at the first stage (inverted index, tf-idf, knn, etc), and subsequent stages rerank the top-K results with higher-latency ML models outputting a relevance score.

I believe progress is currently being made to combine everything into one giant neural model that just ranks everything from the get-go rather than pass in multiple stages.