This work develops a unified fetal heart rate baseline estimation framework using statistical, filtering, and deep-learning methods on the CTU-UHB dataset. The models are evaluated using RMSD.BL, MADI, and acceleration/deceleration agreement. A proposed fusion model integrating uterine contractions enhances robustness under labor events, supporting improved clinical interpretation and diagnostic reliability.