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The heart is important organ of human body part. Life is completely dependent on efficient working of the heart. What if a heart undergoes a disorder, cardiovascular diseases are the most challenging disease for reducing patient count. According to survey conducted by WHO, about 17 million people die around the globe due to cardiovascular diseases ie 29.20% among all caused death, mostly in developing countries. Thus there is a need of getting rid of the this complicated task CVD using advanced data mining techniques, in order to discover knowledge of Heart disease prediction. In this paper, we propose an efficient hybrid algorithmic approach for heart disease prediction. This paper serves efficient prediction technique to determine and extract the unknown knowledge of heart disease using hybrid combination of K-means clustering algorithm and artificial neural network. In our proposed model we considered 14 attribute out of 74 attributes of UCI Heart Disease Data Set [19]. This technique uses medical terms such as age, weight, gender, blood pressure and cholesterol rate etc for prediction. To perform grouping of various attributes it uses k-means algorithm and for predicting it uses Back propagation technique in neural networks. The main objective of this paper is to develop a prototype for predicting heart diseases with higher accuracy rate.