The CRVSM has been built using the Java programming language and Map Reduce, and tests on a dataset of 1.4 million emails have been done. False Positive Rate (FPR), False Negative Rate (FNR), Detection Accuracy (DA), Spam Detection Time (SDT), Spam Detection Rate (SDR), Network Service Ratio (NSR), and Overall Throughput were used to measure CRVSM performance (OT). With improved detection accuracy and in less time, CRVSM reduces the FPR and FNR values while detecting spam emails in vast spaces. To get the best collection of features and lower the dimensionality of the feature space, a unique improved Optimized Feature Selection Protocol (OFSP) has been developed. It works in four phases: Feature Selection Phase, Normalization Phase, Score Assignment Phase, and Optimal Feature Selection Phase. OFSP is a hybrid rule-based technique that combines two well-known feature selection approaches for email spam filtering. The Optimized Vector Search Algorithm (OVSA) is a tool used by OFSP to generate dynamic threshold values. The effectiveness of OFSP was examined through experiments to determine its false positive and false negative rates, detection accuracy, and spam detection time. The outcomes demonstrate that, by obtaining extremely strong performance and producing optimum results, OFSP surpasses CRVSM and PEP. In addition to the experimental investigation, the time complexity of the CRVSM, PEP, and OFSP procedures has been examined using complexity analysis. Results demonstrate that OFSP performs better than CRVSM and PEP protocols by generating much less overhead