Lead generation is the process of turning an outside person or business into a customer of the business. Traditionally, marketing personnel must conduct significant follow-ups in order to convert even one potential consumer. In an effort to convert bad client leads, businesses have burned through the majority of their cash reserves. Due to this, it is now necessary to develop an automated system that can correctly anticipate whether or not a lead should be explored (convert to customer or not). In this study, we attempt to evaluate historical data regarding leads produced by other businesses in order to train and validate an ML/DL model and test it against real-world characteristics to categorize them as hot leads (convert to customers) or cold leads (failed leads). This can be achieved by employing ML algorithms, low code-no code libraries like pycaret in python and make predictions regarding probable lead creation, propensity to convert generated leads, optimal communications team’s actions on the leads. The Supervised ML Algorithms like Logistic Regression, Decision Trees, Random Forests and other models using python library were built to score leads for identifying potential conversions. With good and broad lead scoring models in place they can optimize their CTI actions on the basis of lead prioritization and let go of non-prospect leads at the right time to cut costs and enable efficiency. The result of the study finding reveals that 52% of the sample of 74,779 leads are cold leads and 48% are hot leads that are sales qualified. The leads are qualified using the lead score matrix. As a result, it aids digital businesses in removing unqualified leads and managing the leads better, which raises the caliber of the leads sent to clients. This will improve conversion rates for individual customers. The increased conversion rates support the business strategy of digital marketing firms.