Context. Galaxy clusters and groups are thought to accrete material along the preferred direction of cosmic filaments. These structures have proven difficult to detect because their contrast is low, however, and only a few studies have focused on cluster infall regions. Aims. We detect cosmic filaments around galaxy clusters using photometric redshifts in the range 0.15 <  z <  0.7. We characterise galaxy populations in these structures to study the influence of pre-processing by cosmic filaments and galaxy groups on star formation quenching. Methods. We detected cosmic filaments in the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) T0007 data, focusing on regions around clusters of the AMASCFI CFHTLS cluster sample. The filaments were reconstructed with the discrete persistent structure extractor (DISPERSE) algorithm in photometric redshift slices. We show that this reconstruction is reliable for a CFHTLS-like survey at 0.15 <  z <  0.7 using a mock galaxy catalogue. We split our galaxy catalogue into two populations (passive and star forming) using the LePhare spectral energy density fitting algorithm and worked with two redshift bins (0.15 <  z ≤ 0.4 and 0.4 <  z <  0.7). Results. We showed that the AMASCFI cluster connectivity (i.e. the number of filaments that is connected to a cluster) increases with cluster mass M200. Filament galaxies outside R200 are found to be closer to clusters at low redshift, regardless of the galaxy type. Passive galaxies in filaments are closer to clusters than star-forming galaxies in the low redshift bin alone. The passive fraction of galaxies decreases with increasing clustercentric distance up to d ∼ 5 cMpc. Galaxy groups and clusters that are not located at nodes of our reconstruction are mainly found inside cosmic filaments. Conclusions. These results give clues for pre-processing in cosmic filaments that could be due to smaller galaxy groups. This trend could be further explored by applying this method to larger photometric surveys such as the Hyper Suprime-Cam Subaru Strategic Program (HSC-SPP) or Euclid.