In the context of agroecological transition, breeding females with robust reproductive performance, leading to prolonged lactation sequences, is valuable for farmers. This study aimed to explore the relationship between artificial insemination (AI) success and phenotypic lactation curves that serve as proxies for key biological functions in Alpine and Saanen goats. Using data from two French experimental farms (1996–2021), the study analyzed time series data on milk yield (MY), body weight (BW), and sternal body condition score (BCS_S). These data were modeled at the lactation scale to characterize dynamic profiles and create clusters. Each phenotypic lactation curve was evaluated with three levels of detail: cluster membership, synthetic indicators, and model parameters. To investigate AI success, three datasets were used: 638 lactations with complete MY, BW, and BCS_S data; 1359 lactations with MY and BW; and separate sets with 1731 MY and 795 BCS_S records. A mixed logistic regression model (year as a random effect) assessed the relationship between AI success and phenotypic lactation curve characteristics. Results showed that for primiparous goats, AI success was influenced by MY clusters (p < 0.05), while in multiparous goats, MY and BCS_S clusters did not influence AI success. However, indicators such as persistency (p < 0.001) and BW repletion speed (p < 0.001) were significant. Overall, the lactation curve shape was more important to AI success than milk production level, offering insights for enhancing reproductive performance in dairy goats.