Abstract: Effusion of the knee joint is possibly related to osteoarthritis erupt and is a significant marker of remedial reaction. The investigation is planned for creating and approving a computerized framework dependent on MR imaging for the measurement of joint effusion. The occurrence of knee effusion requires an extensive differential determination and an orderly symptomatic approach. Yearning of the knee effusion is a fundamentally demonstrative and restorative intercession in numerous rheumatologic diseases. The clinical investigation has traditionally included tests counting the patella tap. The precision of these tests for identifying the effusion and measure is not well set up. MR imaging is considered superior for recognizable proof and evaluation of knee effusion. The amount of effusion present in the joint was recorded and MRI criteria for the detection of knee effusion were assessed. The fat cushion division sign was the foremost exact marker of liquid as little as 1-2 mLwas recognized. Axial view of MRI images was used in accessing the knee effusion. The classifier was superior both in terms of time efficiency and classification performance to classifier regularly used on the basis of iterative learning. In this paper we have used two features namely watershed Segmentation and 2-D Gabor Filter. The extracted features from MRI image are given to the classifiers namely Random Forest, Multi Linear BPNN and Adaboost SVM. The random forest classifier was good when comparing with the other two classifier and achieves the good accuracy rate of 92.12%. Finally, the classifier was prevalent both in time adequacy and order execution to the routinely used classifiers dependent on iterative learning.