Now-a-days heart disease is one of the serious disease because most of the people affected by this disease that leads to create death. Due to the serious risk of this heart disease, it has been identified in the beginning stage for avoiding the risk factors. Then the earlier detection system has been created by utilizing optimized and hybridized techniques to recognize the heart disease in earlier stage. So, artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network is introduced to manage the features present in the earlier heart disease classification system. Initially, heart disease related information is collected from Heart Disease Data Set-UCI repository. The collected information is huge in dimension which is difficult to process, that reduces the efficiency of heart disease identification system. So, the dimensionality of the features are reduces according to the behavior of gravitational cuckoo search algorithm. The selected features are processed by above defined associative memory classifier. Then the efficiency of the system is evaluated with the help of MATLAB based experimental results. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.