Photocatalysis can be understood as the acceleration of chemical reactions by incident light. These reactions typically perform poorly without photoactivation and the presence of catalysts. Photocatalysis could find applications in a wide range of fields, such as renewable energy and environmental remediation. The advance in the photocatalysis field is driven by the development of innovative materials allowing a broad range of absorption and efficient charge separation. The most developed photocatalysts are inorganic semiconductors. An alternative approach to increase the efficiency of these photocatalysts is the design of organic–inorganic hybrid materials. Two classes of hybrid materials are largely investigated: 1) small molecules and 2) organic macromolecules or polymers. The polymers can have protective roles as well as photocatalytic and conductive properties. The last type of material is known as conductive polymer. Different types of conductive polymers have been investigated in the literature. This perspective will focus on the new trend in this field by first describing the use of machine learning in designing and understanding polymeric hybrid systems toward catalytic applications, followed by the description of four types of the most used organic–inorganic hybrid materials as photocatalysts based on chitosan, polydopamine, polyaniline and covalent organic frameworks.