supervised learning and labels
The main difference between supervised and unsupervised learning is the following:

In supervised learning you have a set of labelled data, meaning that you have the values of the inputs and the outputs. What you try to achieve with machine learning is to find the true relationship between them, what we usually call the model in math. There are many different algorithms in machine learning that allow you to obtain a model of the data. The objective that you seek, and how you can use machine learning, is to predict the output given a new input, once you know the model.

In unsupervised learning you don't have the data labelled. You can say that you have the inputs but not the outputs. And the objective is to find some kind of pattern in your data. You can find groups or clusters that you think that belong to the same group or output. Here you also have to obtain a model. And again, the objective you seek is to be able to predict the output given a new input. 

Finally, going back to your question, if you don't have labels you can not use supervised learning, you have to use unsupervised learning.