In the current digital environment, copious amounts of data are generated across diverse sectors like healthcare, content creation, the internet, and businesses. ML algorithms are pivotal in analyzing this data to unveil significant ways to make decisions. However, not all features within these datasets are relevant for constructing robust machine learning models. Some features may be insignificant or have minimal impact on the prediction outcomes. By filtering out these irrelevant features, the computational burden on machine learning algorithms is reduced. Using the freely available MINIST dataset, this study explores the application of t-SNE, LDA, and Principal Component Analysis (PCA) alongside several prominent ML techniques like Naive Bayes, SVM classifiers, and K-NN classifications employed. Experimental outcomes illustrate the effectiveness of ML algorithms in this context. Furthermore, the experiments demonstrate that employing PCA with machine learning algorithms leads to improved outcomes, particularly when dealing with high-dimensional datasets. Performance measures like Accuracy 98.34%, Sensitivity 98.76%, Recall 98.45% and Throughput 98.65% have been attained, which was a good improvement.