The importance of image encryption has considerably increased especially after the dramatic evolution of the internet of things (IOT) and due to the simplicity of capturing and transferring digital images. Although there are several encryption approaches, chaos-based image encryption is considered the most appropriate approach for image applications because of its sensitivity to initial conditions and control parameters. This research aims at generating an encrypted image free of statistical information to make cryptanalysis infeasible. Therefore, a new method was introduced in this paper called Multi-layer Chaotic Maps (MLCM) based on confusion and diffusion. Basically, the confusion method uses the Sensitive Logistic Map (SLM), Hénon Map, and the additive white Gaussian noise to generate random numbers to be used in the pixel permutation method. However, the diffusion method uses Extended Bernoulli Map (EBM), Tinkerbell, Burgers, and Ricker maps to generate the random matrix. The correlation between adjacent pixels was minimized to have a very small value $(x 10-3)$. Besides, the keyspace was extended to be very large $left(2^{450} ight)$ considering the key sensitivity to hinder brute force attack. Finally, a histogram was idealized to be perfectly equal in all occurrences and the resulted information entropy was equal to the ideal value(8), which means that the resulted encrypted image is free of statistical properties in terms of histogram and information entropy. Based on the findings, the high randomness of the generated random sequences of the proposed confusion and diffusion methods is capable of producing a robust image encryption framework against all types of cryptanalysis attacks.