The SARS-CoV-2 virus, which causes COVID-19 pandemic has had a profound influence on world health. While most infected individuals experience mild to moderate respiratory infections, some develop post-COVID complications with long-lasting effects. Detecting these complications early is crucial for better patient outcomes. This research explores the possibility of predicting post-COVID complications using patient data and machine learning algorithms. The study reviews existing literature on post-COVID complications and previous research efforts that have utilized machine learning in healthcare. It proposes model selection and training to improve prediction accuracy. The dataset is collected from COVID patients through a survey conducted with random Bangladeshi participants, and data preprocessing techniques are applied. Linear Regression, Decision Trees, Random Forest, and K-Nearest Neighbors are the four machine learning models selected and trained on the dataset. The models' performance is evaluated, and their accuracy and effectiveness in predicting post-COVID complications are compared. The findings show that Decision Trees and Linear Regression have the best accuracy. Overall, this research highlights the potential of machine learning in post-COVID complexity detection and contributes to advancing strategies for managing the pandemic and its long-term effects. The study also proposes some future directions for related work, which could be helpful in long-term research.