In　this　work,　we　study　a　k-Cardinality　Constrained　Regularized　Submodular　Maximization　(k-CCRSM)　problem,　in　which　the　objective　utility　is　expressed　as　the　difference　between　a　non-negative　submodular　and　a　modular　function.　No　multiplicative　approximation　algorithm　exists　for　the　regularized　model,　and　most　works　have　focused　on　designing　weak　approximation　algorithms　for　this　problem.　In　this　study,　we　consider　the　k-CCRSM　problem　in　a　streaming　fashion,　wherein　the　elements　are　assumed　to　be　visited　individually　and　cannot　be　entirely　stored　in　memory.　We　propose　two　multipass　streaming　algorithms　with　theoretical　guarantees　for　the　above　problem,　wherein　submodular　terms　are　monotonic　and　nonmonotonic.