As　an　implementation　tool　of　data　intensive　scientific　research　methods,　machine　learning　(ML)　can　effectively　shorten　the　research　and　development　(R&D)　cycle　of　new　materials　by　half　or　even　more.　ML　shows　great　potential　in　the　combination　with　other　scientific　research　technologies,　especially　in　the　processing　and　classification　of　large　amounts　of　material　data　from　theoretical　calculation　and　experimental　characterization.　It　is　very　important　to　systematically　understand　the　research　ideas　of　material　informatics　to　accelerate　the　exploration　of　new　materials.　Here,　we　provide　a　comprehensive　introduction　to　the　most　commonly　used　ML　modeling　methods　in　material　informatics　with　classic　cases.　Then,　we　review　the　latest　progresses　of　prediction　models,　which　focus　on　new　processing-structure-properties-performance　(PSPP)　relationships　in　some　popular　material　systems,　such　as　perovskites,　catalysts,　alloys,　two-dimensional　materials,　and　polymers.　In　addition,　we　summarize　the　recent　pioneering　researches　in　innovation　of　material　research　technology,　such　as　inverse　design,　ML　interatomic　potentials,　and　microtopography　characterization　assistance,　as　new　research　directions　of　material　informatics.　Finally,　we　comprehensively　provide　the　most　significant　challenges　and　outlooks　related　to　the　future　innovation　and　development　in　the　field　of　material　informatics.　This　review　provides　a　critical　and　concise　appraisal　for　the　applications　of　material　informatics,　and　a　systematic　and　coherent　guidance　for　material　scientists　to　choose　modeling　methods　based　on　required　materials　and　technologies.