Deep learning is the most dominant area to perform the complex challenging tasks such as image classification and recognition. Earlier researchers have been proposed various convolution neural network (CNN) with different architectures to improve the performance accuracy for the classification and recognition of images. However, the fine-tuning of hyper parameters, resulting the optimal network, regularization of parameters is the difficult task. The metaheuristic optimization algorithms are used for solving such kind of problems. In this paper we proffer a fine tune automate CNN with Hybrid Particle Swarm Grey Wolf (HPSGW). This novel algorithm used to discover the optimal parameters of the CNN like batch size, number of hidden layers, number of epochs and size of filters. The proffered optimized architecture is implemented on MNIST, CIFAR are two bench mark datasets and Indian Classical Dance (ICD) for the classification of 8 Indian Classical Dances. The Proffered method improves the model performance accuracy of 97.3% on ICD Dataset, and other benchmark datasets MNIST, CIFAR with an improved accuracy of 99.4% and 91.1%. This auto-tuned network improved the performance by 5.6% for Indian Classical Dance Forms Classification compared to earlier methods and also reduces the computational cost.