Normalization is a preprocessing stage crucial for data manipulation before further processing, especially in fields like soft computing and cloud computing. This paper proposes a new normalization technique called Integer Scaling Normalization (ISN), derived from the AMZD (Advanced on Min-Max Z-score Decimal scaling) method. ISN aims to scale data into the range [0, 1], similar to existing techniques like Min-Max, Z-score, and Decimal scaling. ISN has several features: it scales individual elements, is independent of data size or digit count, and works only with integers. The formula for ISN is Y = (|X| - 10^(n-1)*|A|)/10^(n-1), where X is the data element, n is the number of digits, and A is the first digit of X. ISN is compared with Min-Max normalization using datasets like BSE Sensex, NNGC, and College Enrollment. The results show that ISN effectively scales data within [0, 1]. The proposed technique is applicable to various research areas including soft computing, image processing, and cloud computing. The study concludes that ISN is a viable alternative to existing normalization methods and can be applied in fields requiring data preprocessing, such as time series financial forecasting. The authors are researchers in the fields of financial forecasting, machine learning, and cloud computing.Normalization is a preprocessing stage crucial for data manipulation before further processing, especially in fields like soft computing and cloud computing. This paper proposes a new normalization technique called Integer Scaling Normalization (ISN), derived from the AMZD (Advanced on Min-Max Z-score Decimal scaling) method. ISN aims to scale data into the range [0, 1], similar to existing techniques like Min-Max, Z-score, and Decimal scaling. ISN has several features: it scales individual elements, is independent of data size or digit count, and works only with integers. The formula for ISN is Y = (|X| - 10^(n-1)*|A|)/10^(n-1), where X is the data element, n is the number of digits, and A is the first digit of X. ISN is compared with Min-Max normalization using datasets like BSE Sensex, NNGC, and College Enrollment. The results show that ISN effectively scales data within [0, 1]. The proposed technique is applicable to various research areas including soft computing, image processing, and cloud computing. The study concludes that ISN is a viable alternative to existing normalization methods and can be applied in fields requiring data preprocessing, such as time series financial forecasting. The authors are researchers in the fields of financial forecasting, machine learning, and cloud computing.