In　this　paper,　we　present　a　distributed　machine　learning　algorithm　over　a　network　with　fixed-delay　tolerance.　The　network　is　directed　and　strongly　connected.　The　training　dataset　is　distributed　to　all　agents　in　the　network.　We　combine　the　distributed　convex　optimization　(which　utilizes　double　linear　iterations)　and　corresponding　machine　learning　algorithm.　Each　agent　can　only　access　its　own　local　dataset.　Suppose　the　delay　between　any　pair　of　agents　is　time-invariant.　The　simulation　shows　that　our　algorithm　is　able　to　work　under　delayed　transmission,　in　the　sense　that　over　time　at　each　agent　i　the　ratio　of　the　estimate　value　x(i)(t)　and　scaling　variable　y(i)(t)　can　converge　to　the　optimal　point　of　the　global　cost　function　corresponding　to　the　machine　learning　problem.