Water is crucial for sustaining life. According to the WHO, around 2 billion people in the world do not have access to clean water. Over the years, membrane-based filtration technologies including ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO) have been used effectively to remove toxic ions and desalinate ocean water to make it available for drinking. They consume less energy, are environment-friendly, and are more economical. But these techniques are sensitive to oxidative chemicals, unable to separate microscopic material from water, and are pressure driven. A recently developed technology, Electrospun Nanofibrous Membranes (ENMs), can be used as filtration membranes for desalination and removal of ions and contaminants from water. ENMs are permeable to complex and immense quantities of contaminants in water sources. Besides water treatment, they have extensive applications in agriculture for plant protection, fermentation in the food industry, biocatalytic environmental remediation, biosensing and gene delivery. These membranes are manufactured using an electrospinning technique in which uniaxial stretching of a viscoelastic polymer solution occurs at high voltage. Research aimed at studying the interaction between operating parameters in electrospinning such as membrane fabrication, characterization, performance and the morphology of nanofibres. Principal Component Analysis (PCA) is an unsupervised machine-learning approach used to reduce the problem's dimensionality by making a large number of input variables smaller which are called principal components (PCs). The primary goal of PCA is to retain most of the input features so that while the problem is simplified, the accuracy is also preserved. It is a statistical interpretation of single-value decomposition (SVD). In the data matrix, each row represents measurements of variables from a single experiment. The parameters involved in the electrospinning process are high voltage (kV), liquid entry pressure (kPa), water contact angle (o), needle (spinneret; mm), tip-to-collector distance (cm), dope injection rate (mL•h1), electric field strength, drop height. 23 membranes are distributed into different clusters. Compute the average mean, average matrix meancentered data that describe the decomposition of data matrix into the direction of maximal variance, covariance matrix, and eigenvectors. This simulation is performed with the help of Matlab and Python. In cluster 1, the major impacting factors on membranes are the size and textural conditions. In cluster 2, the major impacting factor on membranes is liquid entry pressure.