When using Bayes' theorem to find the probability of future events along a tree I have had it explained to me that parent events to the child events have already happened? Is this strictly true or can Bayes' theorem be used on two events happening concurrently or so close too each other that they might as well be happening at the same time.
$$\begin{aligned} argmax_c P(w|c)P(c) \end{aligned}$$
Here I am determining the probability that a word is misspelled P(c) being my language model(how likely is word c to be used in English) and P(w|c) being my error model(what is the likelihood that the author wrote the char 'w' when they meant 'c' for a given word).
In this case it is very clearly a case of calculating future potentials. But can we use probability on the present or even the past? Since this current event happened here is the probability that unknown event happened previously or is happening now?
Whoever "explained" this to you didn't understand Bayes' theorem. It has nothing to do with time. It involves conditional probabilities, i.e. probabilities of events under the condition that certain other events occur. Events having occurred in the past is just one (though typical) way how such conditions can come about. Often in $P(X|Y)$ event $Y$ has happened in the past and you want to use this information to update your probability of $X$ occurring, but this is just a typical setup and isn't at all necessary to make sense of conditional probabilities. You can have e.g. $X$ referring to an event in the past and $Y$ to an even in the future; e.g. if you're asking yourself: I don't know whether $X$ has occurred or not; I could do an experiment and see whether $Y$ will occur; if it does, what will that tell me about the probability that $X$ has occurred?