Abstract Ectopic pregnancy (EP) is a condition of incorrect implantation of the fertilized egg outside the uterus. It is one of the major reasons for maternal morbidity and mortality in the first trimester. There are several types of EP depending on the implantation site. Tubal EP is of the most common case of ectopic pregnancy but there is also rare incidence of EP in cervix, abdominal site, and ovaries. Research has shown that the rate of ectopic pregnancies all over the world is 1.9 - 2%. Women who undergo infertility treatments such as IVF or ICSI show higher incidence rates of ectopic pregnancy (2 - 5%). Ectopic pregnancy complicates infertility treatment and early detection is key to device an effective treatment strategy. Algorithmic approaches to diagnosis, exemplified by emerging artificial intelligence and machine learning models, can help in rapid screening and early diagnosis of EP, and are being considered for use by clinicians to make better decisions regarding treatment protocols in recent years. In this study, we perform a survey of literature on different algorithmic approaches and biomarkers that have been used for early and reliable detection of ectopic pregnancy in order to identify the best methods among them. The advantages, disadvantages, and limitation of each study are discussed, and suggestions for further research are provided Abstract Ectopic pregnancy (EP) is a condition of incorrect implantation of the fertilized egg outside the uterus. It is one of the major reasons for maternal morbidity and mortality in the first trimester. There are several types of EP depending on the implantation site. Tubal EP is of the most common case of ectopic pregnancy but there is also rare incidence of EP in cervix, abdominal site, and ovaries. Research has shown that the rate of ectopic pregnancies all over the world is 1.9 - 2%. Women who undergo infertility treatments such as IVF or ICSI show higher incidence rates of ectopic pregnancy (2 - 5%). Ectopic pregnancy complicates infertility treatment and early detection is key to device an effective treatment strategy. Algorithmic approaches to diagnosis, exemplified by emerging artificial intelligence and machine learning models, can help in rapid screening and early diagnosis of EP, and are being considered for use by clinicians to make better decisions regarding treatment protocols in recent years. In this study, we perform a survey of literature on different algorithmic approaches and biomarkers that have been used for early and reliable detection of ectopic pregnancy in order to identify the best methods among them. The advantages, disadvantages, and limitation of each study are discussed, and suggestions for further research are provided