A non-fungible token (NFT) is a unique digital asset stored on the blockchain. With growing interests from investors and collectors, understanding the factors influencing the NFT market is crucial. This study investigates the influence of Twitter social media and trading patterns on transaction volume in the CryptoPunks NFT market. We focus on the influencing factors such as trading behavior, Twitter sentiment, and Twitter features. Based on them, we employ machine learning to predict the CryptoPunks transaction volume. The results reveal a strong correlation between these features and transaction volumes. The proposed PSO-Voting model, which takes all features into consideration, can improve the predictive accuracy significantly, achieving an impressive model accuracy (R2) exceeding 96%. Each feature’s contribution to the transaction volume prediction is also explained. By unveiling the influence of Twitter social media and trading patterns on the CryptoPunks market, this study provides novel insights into the dynamics of the NFT market.