In recent years, the rise of digital technologies has sparked a notable surge in data production, growing at an exponential rate. Clustering techniques are popular in the machine learning. K-means is prominent data clustering technique, It is extensively used due to its straightforward approach and efficiency in dividing data into separate groups by repeatedly assigning each data point to its closest centroid and then updating the centroid positions accordingly. One of the main drawbacks of K-means clustering is its sensitivity to initial centroid selection and identifying the number of clusters. This work presents a comprehensive comparative analysis of three clustering algorithms namely K-means, K-means with Firefly Algorithm (FA), and K-means with Particle Swarm Optimization (PSO) with the help of three clusters and also make use of an incremental dataset.Performance evaluation is carriedout using the performance evaluation metrics such as Silhouette score, Davies-Bouldin score, quantization error, S_Dbw validity index, and SD validity index. The results demonstrate the effectiveness of optimization techniques in enhancing the clustering quality and overcoming the limitations of traditional K-means.