An apparatus for predicting weld quality, the apparatus comprises an image sensor acquiring an image of a welding portion of a workpiece, characterized in that the apparatus further comprising: a storage storing a shape detection model, a depth prediction model and a strength prediction model; and a controller configured to acquire shapes of a melt pool and a keyhole by receiving the image and inputting the image to the shape detection model, acquire a penetration depth of the keyhole by inputting the shapes of the melt pool and the keyhole to the depth prediction model, and acquire a tensile strength by inputting the penetration depth of the keyhole and one of the shape of the melt pool, the shape of the keyhole, or the shapes of both the melt pool and the keyhole to the strength prediction model, wherein the shape detection model is an artificial intelligence model learned using a first learning dataset in which the image of the welding portion is first learning data and the shapes of the melt pool and the keyhole are first label data, and when the image of the welding portion is received, detects and outputs the shapes of the melt pool and the keyhole wherein the depth prediction model is an artificial intelligence model learned using a second learning dataset in which the shapes of the melt pool and the keyhole are second learning data and the penetration depth of the keyhole is second label data, and when the shapes of the melt pool and the keyhole are inputted, predicts and outputs the penetration depth of the keyhole, wherein the strength prediction model is an artificial intelligence model learned using a third learning dataset in which the penetration depth of the keyhole and one of the shape of the melt pool, the shape of the keyhole or the shapes of both the melt pool and the keyhole are third learning data and the tensile strength of the welding portion is third label data, and when the penetration depth of the keyhole and one of the shape of the melt pool, the shape of the keyhole or the shapes of both the melt pool and the keyhole are inputted, predicts and outputs the tensile strength of the welding portion. a storage storing a shape detection model, a depth prediction model and a strength prediction model; and a controller configured to acquire shapes of a melt pool and a keyhole by receiving the image and inputting the image to the shape detection model, acquire a penetration depth of the keyhole by inputting the shapes of the melt pool and the keyhole to the depth prediction model, and acquire a tensile strength by inputting the penetration depth of the keyhole and one of the shape of the melt pool, the shape of the keyhole, or the shapes of both the melt pool and the keyhole to the strength prediction model, wherein the shape detection model is an artificial intelligence model learned using a first learning dataset in which the image of the welding portion is first learning data and the shapes of the melt pool and the keyhole are first label data, and when the image of the welding portion is received, detects and outputs the shapes of the melt pool and the keyhole wherein the depth prediction model is an artificial intelligence model learned using a second learning dataset in which the shapes of the melt pool and the keyhole are second learning data and the penetration depth of the keyhole is second label data, and when the shapes of the melt pool and the keyhole are inputted, predicts and outputs the penetration depth of the keyhole, wherein the strength prediction model is an artificial intelligence model learned using a third learning dataset in which the penetration depth of the keyhole and one of the shape of the melt pool, the shape of the keyhole or the shapes of both the melt pool and the keyhole are third learning data and the tensile strength of the welding portion is third label data, and when the penetration depth of the keyhole and one of the shape of the melt pool, the shape of the keyhole or the shapes of both the melt pool and the keyhole are inputted, predicts and outputs the tensile strength of the welding portion.