This study employs machine learning (ML) algorithms to predict the tensile strength of 3D printed continuous carbon fiber reinforced polyether ether ketone (CF/PEEK) thermoplastic composites, addressing the critical need for rapid and cost-effective material qualification in additive manufacturing. A total of 120 samples were fabricated using fused deposition modeling (FDM), with systematic variations in key printing parameters, including printing path design (unidirectional and orthogonal printing), printing speed (270–600 mm/min), and nozzle temperature (375–410°C). The experimental tensile strength of the composites ranged from 310 MPa to 976 MPa. A Gradient Boosted Decision Tree (GBDT) algorithm was utilized to establish correlations between printing parameters and tensile properties, achieving a comprehensive determination coefficient exceeding 0.8, demonstrating high predictive accuracy. Cross-validation and grid search methods were employed to optimize model parameters, resulting in robust generalization capabilities across training, testing, and validation datasets. Through prediction and validation using ML models, the optimal parameter combination was identified as follows: unidirectional printing, a printing speed of 330 mm/min, and a nozzle temperature of 380°C. The study reveals that the printing path has the most significant impact on tensile strength (relative importance: 0.8), followed by printing speed (0.15) and nozzle temperature (0.05). This research provides an efficient data-driven approach to predict and optimize the mechanical properties of 3D printed composites, aligning with industrial demands for sustainable, lightweight materials in aerospace, automotive, and energy sectors, and offering a scalable solution to accelerate the development and application of 3D printed thermoplastic composites.