An artificial intelligence-based Parkinson's disease diagnosing apparatus, comprising: an image acquiring unit which acquires a first image related to a multi-echo magnitude and phase from an MRI obtained by capturing a brain of a patient; an image processing unit which processes the acquired first image so as to visualize a substantia nigra and a nigrosome-1 region used as imaging biomarkers of the Parkinson's disease; an image analyzing unit which analyzes the processed first image, which includes a plurality of quantitative susceptibility map weighted images, to classify those quantitative susceptibility map weighted images among the plurality, which include the nigrosome-1 region, as a second image from the first image and detects the nigrosome-1 region from the classified second image; and a diagnosing unit which diagnoses whether the patient has the Parkinson's disease by analyzing whether the detected nigrosome-1 region is normal, wherein the image processing unit generates the plurality of quantitative susceptibility map weighted images by applying a quantitative susceptibility map mask to the first image based on a quantitative susceptibility mapping algorithm to perform the processing, and the image processing unit further performs at least one operation of angle adjustment, image enlargement, and reslice, on the generated quantitative susceptibility map weighted images, characterized in that: the image analyzing unit detects a red nucleus and a substantia nigra present in the processed first image through machine learning and classifies those quantitative susceptibility map weighted images among the plurality, which include the nigrosome-1 region, as the second image from the first image based on sequentially checking the quantitative susceptibility map weighted images along the direction of the inferior pole of the red nucleus and selecting an image thereof as the second image at a moment when the red nucleus between the detected red nucleus and the substantia nigra disappears from the first image. an image acquiring unit which acquires a first image related to a multi-echo magnitude and phase from an MRI obtained by capturing a brain of a patient; an image processing unit which processes the acquired first image so as to visualize a substantia nigra and a nigrosome-1 region used as imaging biomarkers of the Parkinson's disease; an image analyzing unit which analyzes the processed first image, which includes a plurality of quantitative susceptibility map weighted images, to classify those quantitative susceptibility map weighted images among the plurality, which include the nigrosome-1 region, as a second image from the first image and detects the nigrosome-1 region from the classified second image; and a diagnosing unit which diagnoses whether the patient has the Parkinson's disease by analyzing whether the detected nigrosome-1 region is normal, wherein the image processing unit generates the plurality of quantitative susceptibility map weighted images by applying a quantitative susceptibility map mask to the first image based on a quantitative susceptibility mapping algorithm to perform the processing, and the image processing unit further performs at least one operation of angle adjustment, image enlargement, and reslice, on the generated quantitative susceptibility map weighted images, characterized in that: the image analyzing unit detects a red nucleus and a substantia nigra present in the processed first image through machine learning and classifies those quantitative susceptibility map weighted images among the plurality, which include the nigrosome-1 region, as the second image from the first image based on sequentially checking the quantitative susceptibility map weighted images along the direction of the inferior pole of the red nucleus and selecting an image thereof as the second image at a moment when the red nucleus between the detected red nucleus and the substantia nigra disappears from the first image.