Apart from all the great answers here I have one additional suggestion and some encouraging words.
A PhD is as much an education as it is a job, perhaps even more so the first. Therefore it is also quite common to follow courses both on general skills needed for a career in academia as well as specific courses on the topic you are working in.
Therefore I would propose to try and find a high quality course on machine learning to familiarize yourself with the field and gain skills to start working with. One that takes 1 to a few weeks at most. I myself have taken such courses on other topics which helped me a lot. If those are not an option, explore online courses.
I have been in a similar situation of feeling utterly lost during my PhD and also afterwards. It wasn't until I taught myself how to program and accomplished developing my own software project that I finally felt I had something to contribute to science and became more confident about my skills. The longer you work in this field, thee better you realize that the learning never ends and also that nearly every scientist has a piece of insecurity inside them. They are only human after all.
Working in academia can be overwhelming and you have to accept that you can never obtain all available knowledge. Especially in the early years of your career this sensation can feel paralyzing and your supervisor hopefully knows or recognizes that feeling too. You will have to find your own way how to not let that stop you and as your learn more and more by trial and error as well as small successes you will become more confident. With this gain in knowledge, experience and confidence you will also start having your own ideas and opinions and spot gaps in current knowledge more easily, but these things take time.
The most important thing now is to work on obtaining enough knowledge to understand what it is you are supposed to do, and formulate for yourself why you are doing it, why it is relevant. The latter can help you feel more confident and motivated to achieve your goals. This will also help you determine whether the goals set are realistic or an insurmountable mountain.
I do not know how strong your supervisor is skilled in this field. Sometimes it happens that projects are set up with goals that require skills which are outside the range of expertise of the PI, and they possibly underestimated how challenging the task is. So if this is the case for your supervisor, it could be that she not purely overestimated your abilities, but also or instead underestimated the difficulty of the research project for someone with no or little experience with the techniques (machine learning in this case).
A healthy dialogue with your supervisor is therefore crucial to identify obstacle of all the varieties I described above early on in order to take timely steps to either fill the gaps in knowledge, identify limitations, evaluate how realistic the goals are and if needed adjust the course of the project towards a successful compromise if possible.