We introduce a class of backward stochastic differential equations (BSDEs) on the Wasserstein space of probability measures. This formulation extends the classical correspondence between BSDEs, stochastic control, and partial differential equations (PDEs) to the mean--field (McKean--Vlasov) setting, where the dynamics depend on the law of the state process. The standard BSDE framework becomes inadequate in this context, motivating a new definition in terms of measure--dependent solutions. Under suitable assumptions, we demonstrate that this formulation is in correspondence with both mean--field control problems and partial differential equations defined on the Wasserstein space. A comparison principle is established to ensure uniqueness, and existence results are obtained for generators that are linear or quadratic in the $z$--variable. This framework provides a probabilistic approach to control and analysis on the space of probability measures.