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The premier goal of this research is to develop the Fuzzy Deep Neural Sliding Mode Fractional Order Proportional Integral Derivative (FDN-SM-FOPID) controller system for controlling liquid in quadruple spherical tank systems. This is used in non-linear spherical systems to control the level of liquid in real time. These models' dynamics allow for a more accurate identification of the spherical tank system that generates control signals from liquid samples obtained at reference levels. However, because the system is susceptible to outside disturbances, error minimization is not done. Therefore, it requires the addition of a special controller to lessen this flaw. The suggested Deep Neural Fuzzy model's six-layered network is optimized using the back-propagation method. As a result, the system's efficient training reduces offset model errors, steady state errors, and unmeasured disturbances. The liquid level is maintained …