Alzheimer＇s　disease　(AD)　is　a　complex　form　of　dementia　and　due　to　its　high　phenotypic　variability,　its　diagnosis　and　monitoring　can　be　quite　challenging.　Biomarkers　play　a　crucial　role　in　AD　diagnosis　and　monitoring,　but　interpreting　these　biomarkers　can　be　problematic　due　to　their　spatial　and　temporal　heterogeneity.　Therefore,　researchers　are　increasingly　turning　to　imaging-based　biomarkers　that　employ　data-driven　computational　approaches　to　examine　the　heterogeneity　of　AD.　In　this　comprehensive　review　article,　we　aim　to　provide　health　professionals　with　a　comprehensive　view　of　past　applications　of　data-driven　computational　approaches　in　studying　AD　heterogeneity　and　planning　future　research　directions.　We　first　define　and　offer　basic　insights　into　different　categories　of　heterogeneity　analysis,　including　spatial　heterogeneity,　temporal　heterogeneity,　and　spatial-temporal　heterogeneity.　Then,　we　scrutinize　22　articles　relating　to　spatial　heterogeneity,　14　articles　relating　to　temporal　heterogeneity,　and　five　articles　relating　to　spatial-temporal　heterogeneity,　highlighting　the　strengths　and　limitations　of　these　strategies.　Furthermore,　we　discuss　the　importance　of　understanding　spatial　heterogeneity　in　AD　subtypes　and　their　clinical　manifestations,　biomarkers　for　abnormal　orderings　and　AD　stages,　the　recent　advancements　in　spatial-temporal　heterogeneity　analysis　for　AD,　and　the　emerging　role　of　omics　data　integration　in　advancing　personalized　diagnosis　and　treatment　for　AD　patients.　By　emphasizing　the　significance　of　understanding　AD　heterogeneity,　we　hope　to　stimulate　further　research　in　this　field　to　facilitate　the　development　of　personalized　interventions　for　AD　patients.