Methods and systems for predicting a price of any subtractively manufactured part utilizing artificial intelligence at a computing device

A method for predicting a price of any subtractively manufactured part utilizing artificial intelligence at a computing device, the method of predicting a price comprising:

• receiving a manufacturing request datum, wherein the manufacturing request datum further comprises at least an element of user mechanical part data;
• selecting a correlated dataset containing a plurality of data entries wherein each dataset contains at least a datum of mechanical part data and at least a first correlated compatible part element as a function of the manufacturing request datum;
• generating, at a clustering unsupervised machine-learning model, at least a second correlated compatible part element as a function of the manufacturing request datum and the correlated dataset;
• receiving, at a supervised machine-learning model, a first training set as a function of the at least an element of user mechanical part data and the at least a second correlated compatible part element;
• generating, at a supervised machine-learning model as a function of the first training set, an external price output as a function of relating the manufacturing request datum to at least a part element;
• determining a price ratio output, as a function of the external price output, wherein determining the price ratio output further comprises:
• generating a loss function of a part specific variable;
• and
• minimizing the loss function.