"From what I understand is the repeated iteration will take random weights and at some point those weights will be kinda perfect for the given task (plz correct me if i'm wrong)"

You're at least somewhat wrong - it's not all random. The weights are indeed initialized randomly, but then adjusted to fit batches of training data. The weights are updated to more closely match the data. This is usually done through stochastic gradient descent and leverages the difference between your network's current predictions and the known ground truth as calculated using the chosen loss function (e.g. the mean square error or binary cross-entropy).