Cardiovascular diseases （CVDs） are among the leading cause of global morbidity and mortality. Due to their high prevalence and often asymptomatic progression， there is a pressing need for diagnostic tools that enable the early， accurate， and accessible detection of them. Acute coronary syndrome （ACS）， as a common and severe CVDs with high morbidity and mortality rates， has attracted considerable scientific interest. Various methods have been developed to detect ACS rapidly and accurately. Traditional diagnostic methods relying on antibody-based assays are effective. However， they face significant limitations， including high production costs， poor stability under varying environmental conditions， batch-to-batch variability， and cross-reactivity leading to false positives. These challenges have motivated the search for robust， cost-effective alternatives capable of detecting biomarkers with high sensitivity and specificity. Molecularly imprinted polymers （MIPs） have emerged as a promising alternative solution， offering antibody-like molecular recognition capabilities， superior stability， lower production costs， and resistance to harsh environmental conditions. This review systematically examines the latest advancements in MIP-based sensors for ACS biomarker detection in the last fifteen years， including imprinting strategies for key ACS biomarkers， sensor development and integration， and current challenges along with future perspectives. The first section focuses on the molecular imprinting techniques for essential ACS biomarkers， such as cardiac troponin （cTnI/cTnT）， myoglobin （Myo）， and creatine kinase （CK）. It compares whole-protein imprinting with epitope imprinting， highlighting the advantages of the latter in reducing template costs and enhancing binding specificity. Epitope imprinting using short peptide sequences has demonstrated femtomolar detection limits while overcoming challenges associated with large protein templates， such as structural denaturation and difficult template removal. The review also explores innovative approaches like dummy template imprinting， where structurally similar but cheaper molecules are used to create MIPs for high-cost biomarkers， achieving comparable specificity and sensitivity. The second section discusses the integration of MIPs with advanced biosensing platforms. Electrochemical sensors， using MIP-modified electrodes， have achieved remarkable sensitivity and rapid response times， making them suitable for point-of-care testing （POCT）. Optical sensors， particularly those based on surface-enhanced Raman spectroscopy and surface plasmon resonance， enable label-free， real-time detection with ultra-low detection limits. The review also addresses the integration of MIPs with microfluidic technology， where miniaturized devices facilitate automated， high-throughput biomarker analysis. Examples include paper-based microfluidic sensors that combine capillary action with MIP-SERs tags for multiplexed detection， achieving low detection limits without complex instrumentation. Despite these advancements， the review identifies key challenges hindering widespread clinical adoption of the MIP’s based ACS sensor. Although the sensitivity and specificity of MIPs are impressive， they still lag behind those of monoclonal antibodies in some applications， particularly for low-abundance biomarkers. Reproducibility issues arise from variations in polymerization conditions and template removal efficiency. Commercialization barriers include the lack of standardized production protocols and regulatory frameworks for MIP-based diagnostics. The review proposes several strategic directions to address these limitations. Computational modeling and machine learning could optimize monomer selection and polymerization conditions to enhance MIP’s performance. The development of hybrid systems combining MIPs with nanomaterials may further improve sensitivity and signal transduction. Multidisciplinary collaborations among chemists， engineers， and clinicians will be essential to translate laboratory innovations into commercially viable diagnostic tools. Additionally， the integration of MIPs with artificial intelligence machine learning algorithms could support the development of personalized diagnosis and treatment strategies. These future perspectives are likely to have a significant impact on the early diagnosis and treatment of cardiovascular diseases. In conclusion， MIP-based sensors represent a promising direction in ACS diagnostics， offering a unique combination of affordability， stability， and precision. By addressing current technical and translational challenges， MIP technology has the potential to revolutionize early disease detection， particularly in resource-limited areas. This review not only summarizes a decade of research progress but also provides a plan for future developments that could make personalized， decentralized cardiovascular diagnostics a widespread reality.