<p>This paper discusses the synchronization problem of stochastic fuzzy cellular neural networks (FCNNs) involving the uncertainty, stochastic disturbances, and time delays. The objective of this study is to enhance the CNN model in terms of fuzzy-logic in which the information are processed through neighboring cells considering the fuzzy minimum maximum templates. The factors such as uncertainties, stochastic disturbances, time-delays are considered into the FCNN models. This study proposes an adaptive intermittent control (AIC) that can ensures the synchronization between FCNN model with and without external control input. Distinct from the existing works on synchronization problem, this study considers that the control actions are performed in interval manner, which can reduce the packet loss and data leakages. This paper can ensure the synchronization process through sufficient stability conditions, which can guarantee the convergence of error model towards origin. Besides, this study derives an analytical expression that shows the time of synchronization, irrespective of uncertainties and stochastic disturbances. In this regard, the Lyapunov stability theory (LST) and Ito’s calculus theory are employed to derive those stability conditions in the form of inequalities. This study also compares the performance of CNN and FCNN in terms of synchronization by considering two-neuron and three-neuron models with the help of numerical simulations. The efficacy of proposed model is validated through theoretical frameworks and numerical simulations for CNN and FCNN models. This study shows that the proposed FCNN model can exhibits the chaotic behavior, which can be used as pseudo random number generator while degrade the cryptosystem. Besides, this study demonstrates the performance of desired image encryption and decryption algorithms with the key sensitivity analysis, histogram analysis, entropy analysis, and correlation analysis.</p>