PURPOSE: This study aims to systematically assess the surgical outcomes and postoperative recovery discrepancies between Robotic-Assisted Total Knee Arthroplasty (RA-TKA) and Conventional Total Knee Arthroplasty (C-TKA) using machine learning algorithms. The objective is to analyze the advantages and disadvantages of both techniques across various parameters and propose optimization recommendations. METHODS: Data from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) clinical database were collected and underwent thorough cleaning and standardization. Key variables such as operative time, Length of Stay (LOS), and postoperative functional status were extracted for analysis. A predictive model was developed and trained using the random forest machine learning algorithm based on postoperative recovery data. The model's performance was validated using a test dataset, and statistical analyses were conducted to compare the surgical outcomes and postoperative recovery between RA-TKA and C-TKA. RESULTS: The machine learning model's predictions indicate that RA-TKA surpasses C-TKA in all surgical outcome metrics, exhibiting superior means and variances. Furthermore, RA-TKA demonstrates better postoperative functional status, lower Complication Rate (CR), and a higher modified frailty index (mFI), suggesting enhanced and quicker recovery for RA-TKA patients. CONCLUSION: The evaluation results derived from machine learning algorithms suggest that RA-TKA may offer advantages over C-TKA in several crucial metrics. These findings provide valuable insights that could inform future efforts to optimize surgical procedures and postoperative care in clinical practice.