The paper introduces a new normalization technique called Integer Scaling Normalization (AMZD), which is designed to scale and transform data within the range of 0 to 1. The authors, S. Gopal Krishna Patro and Kishore Kumar Sahu, highlight the importance of normalization in various fields such as soft computing and cloud computing. They discuss existing normalization techniques like Min-Max normalization, Z-score normalization, and Decimal scaling, and then propose their new method. The AMZD technique is characterized by its ability to handle individual element scaling, independence from data size and amount, and applicability to integer numbers only. The paper includes a detailed explanation of the AMZD formula and compares it with the Min-Max normalization technique using datasets from BSE Sensex, NNGC, and college enrollment data. The comparison is presented through tables and graphs, demonstrating the effectiveness of the proposed technique. The authors conclude that the AMZD technique can be widely applied in various research areas, including financial forecasting and time series analysis.The paper introduces a new normalization technique called Integer Scaling Normalization (AMZD), which is designed to scale and transform data within the range of 0 to 1. The authors, S. Gopal Krishna Patro and Kishore Kumar Sahu, highlight the importance of normalization in various fields such as soft computing and cloud computing. They discuss existing normalization techniques like Min-Max normalization, Z-score normalization, and Decimal scaling, and then propose their new method. The AMZD technique is characterized by its ability to handle individual element scaling, independence from data size and amount, and applicability to integer numbers only. The paper includes a detailed explanation of the AMZD formula and compares it with the Min-Max normalization technique using datasets from BSE Sensex, NNGC, and college enrollment data. The comparison is presented through tables and graphs, demonstrating the effectiveness of the proposed technique. The authors conclude that the AMZD technique can be widely applied in various research areas, including financial forecasting and time series analysis.