Streaming　fair　submodular　maximization　is　attracting　considerable　research　interest　due　to　its　broad　applications　in　machine　learning,　particularly　for　tasks　such　as　feature　selection　and　text　summary　against　large-scale　data　and　fairness　considerations.　Given　a　sequence　of　data　points　belonging　to　distinct　groups　and　arriving　in　a　streaming　manner,　the　problem　aims　to　select　k　data　points　from　the　stream　to　maximize　the　total　revenue　of　the　selected　points.　In　this　paper,　we　first　devise　an　efficient　(12-ε)-approximation　algorithm　with　O(log(1εlogkε))　passes,　an　improvement　over　the　previous　O(1εlogkε)　passes.　Then,　we　present　a　13-ε-approximation　algorithm　that　needs　only　one　pass　and　consumes　a　buffer　of　size　O(k+|B|)　and　achieves　a　ratio　strictly　greater　than　14　while　using　a　buffer　of　size　O(klogk).　Lastly,　we　conduct　extensive　experiments　using　real-world　datasets　to　validate　our　method,　demonstrating　that　it　outperforms　all　state-of-the-art　algorithms　in　terms　of　efficiency,　effectiveness,　and　scalability.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.